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2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS) June 28 – July 1, 2015 Las Vegas, USA Editors: Takayuki Ito Yanggon Kim Naoki Fukuta Sponsored by IEEE Computer Society URL: http://www.computer.org International Association for Computer & Information Science (ACIS) URL: www.acisinternational.org IEEE Catalog Number: CFP15CIS-USB ISBN: 978-1-4799-8678-1 Copyright and Reprint Permission: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For other copying, reprint or republication permission, write to IEEE Copyrights Manager, IEEE Operations Center, 445 Hoes Lane, Piscataway, NJ 08854. All rights reserved. Copyright ©2015 by IEEE. Published by ACIS International 735 Meadowbrook Mt Pleasant, MI 48858 Phone: 989-774-3811 Email: [email protected] Web Site: www.acisinternational.org IEEE Catalog Number: CFP15CIS-ART ISBN: 978-1-4799-8679-8 Table of Contents KEYNOTE 1 MOOCs, MOOE and MOOR In China Wenai Song . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 COMMUNICATION SYSTEMS & NETWORKS 3 SONET over RPR Ammar Hamad, Michel Kadoch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Quantifying Security Risk by Measuring Network Risk Conditions Candace Suh-Lee, Juyeon Jo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Feasibility Analysis for Incorporating/Deploying SIEM for Forensics Evidence Collection in Cloud Environment Muhammad Irfan, Haider Abbas, Waseem Iqbal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15 Mining Information Assurance Data with a Hybrid Intelligence/Multi-agent System Charles Fowler, Robert, II Hammell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Secure in-vehicle Systems against Trojan Attacks Masaya Yoshikawa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Social Routing: A Novel Routing Protocol for Delay Tolerant Network based on Dynamic Connectivity Viet Quoc Nguyen, Van Phuoc Pham, Quoc Son Trinh, Lung Vu Duc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35 A Big Data Approach to Enhance the Integration of Access Control Policies for Web Services Mohammed Alodib, Zaki Malik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 COPE: Cooperative Power and Energy-efficient Routing Protocol for Wireless Sensor Networks Saima Jamil, Saqib Jamil, Sheeraz Ahmed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47 Comparative Analysis on the Signature Algorithms to Validate AS Paths in BGPsec Kyoungha Kim, Yanggon Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53 Opportunistic Wireless Network Coding Based On Small-time Scale Traffic Prediction Rui Zhang, Jie Li, Quan Qian, Wei Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Web-based Motion Detection System for Health Care Ruiling Gao, Minghuan Zhao, Zhihui Qiu, Yingzhou Yu, C. Hwa Chang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 A Novel Contention Window Backoff Algorithm for IEEE 802.11 Wireless Networks Ikram Syed, Byeong-Hee Roh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71 A Method for Secure RESTful Web Service Sungchul Lee Lee, Ju-Yeon Jo, Yoohwan Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Deploying Agents in the Network to Detect Intrusions Shankar Banik, Luis Pena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83 Empirical Evaluation of Designing Multicasting Network with Minimum Delay Variation Nicklaus Rhodes, Shankar Banik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 An Autonomous Model to Enforce Security Policies Based on User’s Behavior Kambiz Ghazinour, Mehdi Ghayoumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 CONTROL SYSTEMS, INTELLIGENT SYSTEMS 101 Utilizing NFC to Secure Identification Robert Gripentog, Yoohwan Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 A Kernel based Atanassov’s Intuitionistic Fuzzy Clustering for Network Forensics and Intrusion Detection Anupam Panwar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Regimdroid : Framework for Customize Android Platform to act as a Brain for Telepresence Robot Nouha Ghribi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Communication System Based on Chaotic Delayed Feedback Oscillator with Switched Delay Mikhail Prokhorov, Danil Kulminskiy, Anatoly Karavaev, Anatoly Karavaev, Vladimir Ponomarenko . . . . . . 119 Effective Gaze Writing with Support of Text Copy and Paste Reo Kishi, Takahiro Hayashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 A Review of Multimodal Biometric Systems: Fusion Methods and Their Applications Mehdi Ghayoumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .131 An Adaptive Fuzzy Multimodal Biometric System for Identification and Verification Mehdi Ghayoumi, Kambiz Ghazinour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Evaluating a GA-based Approach to Dynamic Query Approximation on an Inference-enabled SPARQL Endpoint Yuji Yamagata, Naoki Fukuta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .143 COMPUTER ARCHITECTURE AND VLSI 149 Fully Pipelined VLSI Architecture of a Real-Time Block-Based Object Detector for Intelligent Video Surveillance Systems Min-Chun Tuan, Shih-Lun Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149 A Compact Design of n-Bit Ripple Carry Adder Circuit using QCA Architecture Nusrat Jahan Lisa, Tania Sultana Rimy, Rajon Bardhan, Tangina Firoz Bithee, Zinia Tabassum . . . . . . . . . . .155 Using SPIN to Check Simulink Stateflow Models Chikatoshi Yamada, Michael Miller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .161 Fast Bootstrapping Method for the Memory-Disk Integrated Memory System Sangjae Nam, Su-Kyung Yoon, Shin-Dug Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .167 Selective Data Buffering Module for Unified Hybrid Storage System Kihyun Park, Kihyun Park, Shin-Dug Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .173 DATA MINING, DATA WAREHOUSING & DATABASE 179 Automated Generation of Hierarchic Image Database with Hybrid Method of Ontology and GMM-based Image Clustering Ryosuke Yamanishi, Ryoya Fujimoto, Yuji Iwahori, Robert J Woodham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .179 Multivariate Temporal Link Prediction in Evolving Social Networks Alper Ozcan, ¸Sule Gunduz Ögüdücü . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .185 A Graph Model Based Author Attribution Technique for Single-Class Email Classification A Novino Nirmal, Kyung-Ah Sohn, Tae-Sun Chung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .191 Introducing the Concept of “Always-Welcome Recommendations” Edson B. dos Santos Junior, Rafael M. D’Addio, Arthur F. da Costa, Marcelo G. Manzato, Rudinei Goularte 197 CBDIR: Fast and Effective Content Based Document Information Retrival System Moon Soo Cha, So Yeon Kim, Jae Hee Ha, Min-June Lee, Young-June Choi, Kyung-Ah Sohn . . . . . . . . . . . . . 203 Mobile Phone Span Image Detection based on Graph Partitioning with Pyramid Histogram of Visual Words Image Descriptor So Yeon Kim, Kyung-Ah Sohn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .209 Multi-purpose Adaptable Business Tier Components Based on Call Level Interfaces Óscar Mortágua Pereira, Rui Aguiar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .215 Implementation of Modified Overload Detection Technique with VM Selection Strategies Based on Heuristics and Migration Control Mohammad Rashedur Rahman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .223 A Feature Selection Method for Comparision of Each Concept in Big Data Takafumi Nakanishi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Searching Human Actions based on a Multi-dimensional Time Series Similarity Calculation Method Yu Fang, Kosuke Sugano, Kenta Oku, Hung-Hsuan Huang, Kyoji Kawagoe . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 A Human Behavior Processes Database Prototype System for Surgery Support Zhang Zuo, Kenta Oku, Hung-Hsuan Huang, Kyoji Kawagoe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .241 Clustered Based VM Placement Strategies Mohammad Rashedur Rahman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .247 A Personalized Music Discovery Service based on Data Mining Mahfuzur Rahman Siddiquee, Saifur Rahman, Naimul Haider, Shahnewaz Ul Islam Chowdhury, Mohammad Rashedur Rahman, Sharnendu Banik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .253 Generalized Entropy based Semi-Supervised Learning Taocheng Hu, Jinhui Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Investigation of Localized Sentiment for a Given Product by Analyzing Tweets Syed Akib Anwar Hridoy, Faysal Ahmed, Mohammad Samiul Islam, M. Tahmid Ekram, Mohammad Rashedur Rahman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .265 KNOWLEDGE DISCOVERY, NEURAL NETWORKS AND GENETIC ALGORITHMS 271 Enhancing the Impact of Science Data: Toward Data Discovery and Reuse Alan Chappell, Jesse Weaver, Sumit Purohit, William Smith, Karen Schuchardt, Patrick West, Benno Lee, Peter Fox. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 A Pruning Algorithm for Reverse Nearest Neighbors in Directed Road Networks Rizwan Qamar, Muhammad Attique, Tae-Sun Chung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 A Semantic Approach for Transforming XML Data to RDF triples Mohamed Kharrat, Anis Jedidi, Faiez Gargouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 SPEECH AND SIGNAL PROCESSING 291 Syllable-based Myanmar Language Model for Speech Recognition Wunna Soe, Yadana Thein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 In-house Alert Sounds Detection and Direction of Arrival Estimation to Assist People with Hearing Difficulties Mohammad Daoud, Mahmoud Al-Ashi, Fares Abawi, Ala Khalifeh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Nearest Multi-Prototype Based Music Mood Classification Babu Baniya, Joonwhoan Lee, Choong Seon Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .303 IMAGE PROCESSING & PATTERN RECOGNITION 307 Decomposition of Partly Occluded Objects Based on Evaluation of Figural Goodness Takahiro Hayashi, Tatsuya Ooi, Motoki Sasaki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .307 Using Ant’s Colony Algorithm for Improved Segementation for Number Plate Recognition Shantanu Prakash, Sanchay Dewan, Shreyansh Bajaj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .313 FPGA Implementation of a Low Complexity Steganographic System for Digital Images Williams Antonio Pantoja Laces, Jose Juan Garcia-Hernandez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Recognition of Offline Handwritten Hindi Text Using Middle Zone of the Words Naresh Garg, Lakhwinder Kaur, Mansih Jindal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Automatic Extraction of Text Regions from Document Images by Multilevel Thresholding and K-means Clustering Vu Hoai Nam, Tran Tuan Anh, Na In Seop, Kim Soo-Hyung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .329 Automated Thresholding of Lung CT Scan for Artificial Neural Network based Classification of Nodules Sheeraz Akram, Muhammad Younus Javed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 INTELLIGENT AGENT TECHNOLOGY, AGENT BASED SYSTEMS 341 Single-Object Resource Allocation in Multiple Bid Declaration with Preferential Order Kengo Saito, Toshiharu Sugawara . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .341 HMM-Based Vietnamese Speech Synthesis Son Trinh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .349 A Scoring Rule-based Truthful Demand Response Mechanism Keisuke Hara, Takayuki Ito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Multiagent-based Distributed Backup System for Individuals Takahiro Uchiya, Motohiro Shibakawa, Tetsuo Kinoshita, Ichi Takumi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .361 INTERNET TECHNOLOGY AND APPLICATIONS, E-COMMERCE 367 eMedicalHelp: A Customized Medical Diagnostic Application: Is a single questionnaire enough to masure stress? Hedieh Ranjbartabar, Amir Maddah, Manolya Kavakli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Development of Mobile Voice Navigation System Using User-Based Mobile Maps Annotations Tomohiro Yanagi, Daisuke Yamamoto, Naohisa Takahashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 3D Web Applications in E-Commerce: A Secondary Study on the Impact of 3D Product Presentations Created with HTML5 and WebGL Jens Geelhaar, Gabriel Rausch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 SQUED: A Novel Crowd-sourced System for Detection and Localization of Unexpected Events from SmartphoneSensor Data Taishi Yamamoto, Kenta Oku, Hung-Hsuan Huang, Kyoji Kawagoe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Some Observations On Online Advertising: A New Advertising System Dapeng Liu, Simon Xu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 MANAGEMENT INFORMATION SYSTEMS 393 Objective Framework for Early-Stage Comparison of Software Development Project Types Donghwoon Kwon, Robert Hammell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 MIDDLEWARE ARCHITECTURES & TECHNIQUES 399 Dual RAID Techniques for Ensuring High Reliability and Performance in SSD Sohyun Koo, Sunsoo Kim, Tae-Sun Chung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 A Novel Architecture for Learner’s Profiles Interoperability Leila Ghorbel, Corinne Amel Zayani, Ikram Amous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 A Multimedia-Oriented Digital Ecosystem: a New Collaborative Environment Solomon Asres Kidanu, Yudith Cardinale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .411 Dynamic Binary Translation in a Type-II Hypervisor for CAVIUM MIPS64 Based Systems Qurrat Ulain, Usama Anwar, Asad Raza, Abdul Qadeer, Ghulam Mustafa, Abdul Waheed . . . . . . . . . . . . . . . 417 MOBILE/WIRELESS COMPUTING 423 An Energy-saving Task Scheduler for Mobile Devices Hao Qian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .423 Challenges and Implementation of ad-hoc Water Gauge System for the Grasp of Internal Water Damage Takanobu Otsuka, Yoshitaka Torii, Takayuki Ito . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .431 Energy-Efficient Distributed Computing Solutions for Internet of Things with ZigBee Devices Grzegorz Chmaj, Henry Selvaraj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 PARALLEL AND DISTRIBUTED COMPUTING & SYSTEMS 443 Emerald: Enhance Scientific Workflow Performance with Computation Offloading to the Cloud Hao Qian, Daniel Andresen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 PROGRAMMING LANGUAGES, COMPILERS, & OPERATING SYSTEMS 449 Accelerating Storage Access by Combining Block Storage with Memory Storage Shuichi Oikawa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .449 SOFTWARE SPECIFICATION TECHNIQUES 455 Formal Specification and Reasoning for Situated Multi-agent System Zhuang Li, Huaikou Miao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .455 WEB ENGINEERING & APPLICATIONS 461 Automatic Generation of Programming Exercises for Learning Programming Language Akiyoshi Wakatani, Toshiyuki Maeda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .461 SPECIAL SESSION 1 467 Robust Location Tracking Method for Mixed Reality Robots using a Rotation Search Method Masahiro Yamamoto, Kazuhiro Suzuki, Ryosuke Ogawa, Nobuhiro Ito, Yoshinobu Kawabe . . . . . . . . . . . . . . .467 Verifying Ignition Timing of Gasoline Direct Injection Engine’s PCM Masato Yamauchi, Nobuhiro Ito, Yoshinobu Kawabe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Analysis of Driving Behaviors based on GMM by using Driving Simulator with Navigation Plugin Naoto Mukai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Analyzing Relationship between the Number of Errors in Review Processes for Embedded Software Development Projects Toyoshiro Nakashima, Kazunori Iwata, Yoshiyuki Anani, Naohiro Ishii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .485 Classification on Nonlinear Mapping of Reducts Based on Nearest Neighbor Relation Naohiro Ishii, Ippei Torii, Naoto Mukai, Kazunori Iwata, Toyoshiro Nakashima . . . . . . . . . . . . . . . . . . . . . . . . 491 WORKSHOP I 497 Proposal of Programming Creation Application Using Road Signs by Smartphones Reiko Kuwabara, Eigo Ito, Takayuki Fujimoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Proposal of Multiple Travel Scheduling System based on Inverse Operation Method Murata Kazuya, Takayuki Fujimoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .503 A proposal of Programming Education System using Mechanical Calculator Mechanism Eigo Ito, Takayuki Fujimoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .509 SDSS: Proposal on Feeding Support Application Software which Enables the User to Create a State of “Mental Alertness” Motoichi Adachi, Takayuki Fujimoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 A Proposal of the System to Stop a Decline of the Interest of Great East Japan Earthquake Koji Fujita, Takayuki Fujimoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .519 WORKSHOP II 525 Singing Voice Detection of Popular Music Using Beat Tracking and SVM Fengyan Wu, Shutao Sun, Jianglong Zhang, Yongbin Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .525 Solving the Supermarket Shopping Route Planning Problem Based on Genetic Algorithm Xiajia Chen, Ying Li, Tao Hu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .529 Online Advertising Demand-side Platform Business System Design Exploration Tao Lei, Junpeng Gong, Yujun Wen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .535 A Feature Selection Algorithm of Music Genre Classification Based on ReliefF and SFS Meimei Wu, Yongbin Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .539 Research and Implementation of Four-prime RSA Digital Signature Algorithm Zhenjiu Xiao, Yongbin Wang, Zhengtao Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .545 The Design and Implementation of Personalized News Recommendation System Xuejiao Han, Wenqian Shang, Shuchao Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 551 A Logic Model of Interest in Information Network Shiping Zhou, Wei Zhang, Xiangrong Tong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Research on Public Opinion Based on Big Data Songtao Shang, Minyong Shi, Wenqian Shang, Zhiguo Hong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 An Automatic Semantic Web Service Composition Method Based on Ontology Ying Li, Yulong Li, Tao Hu, Zhisheng Lv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 The Maximal Operator Classifier Yuqi Wang, Wenqian Shang, Shuchao Feng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .567 Visualization in Media Big Data Analysis Yingjian Qi, Xinyan Yu, Guoliang Shi, Ying Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .571 Personalized two party key exchange protocol Tong Yi, Minyong Shi, Wenqian Shang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .575 Personalized News Recommendation Based on Links of Web Zhenzhong Li, Wenqian Shang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 Interactive Virtual Theater Display System Min Feng, Huaichang Du . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .585 An Improved Algorithm for Active Contour Extraction Based on Greedy Snake Hui Ren, Zhibin Su, Chaohui Lv, Fangjv Zou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .589 Spread Influence Algorithm Of News Website Based on PageRank GuoWei Chen, Fei Xie, Tao Lei, Yu Su . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .593 SERA 2015 597 Petri Nets-based Design of Real-Time Reconfigurable Networks on Chips Hela Ben Salah, Adel Benzina, Mohamed Khalgui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .597 Real-Time Reconfigurable Scheduling of Multiprocessor Embedded Systems Using Hybrid Genetic Based Approach Hamza Gharsellaoui, Ismail Ktata, Naoufel Kharroubi, Mohamed Khalgui . . . . . . . . . . . . . . . . . . . . . . . . . . . .605 New Adaptive Middleware for Real-Time Embedded Operating Systems Fethi Jarray, Hamza Chniter, Mohamed Khalgui. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .610 SABPEL: Creating Self-Adaptive Business Processes Sihem Cherif, Raoudha Ben Djemaa, Ikram Amous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .619 A Learning Semantic Web Service for Generating Learning Paths Chaker Ben Mahmoud, Ikbel Azaiez, Fathia Bettahar, Marie-Hélène Abel, Faïez Gargouri . . . . . . . . . . . . . . . 627 2LPA-RTDW: A Two-Level Data Partitioning Approach for Real-time Data Warehouse Issam Hamdi, Emna Bouazizi, Saleh Alshomrani, Jamel Feki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .632 Opus Framework: A Proof-of-Concept Implementation Nahla Haddar, Mohamed Tmar, Faiez Gargouri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .639 A Service-Oriented Architecture (SOA) Framework for Choreography Verification Sirine Rebai, Hatem Hadj Kacem, Mohamed Karaa, Saul E. Pomares, Ahmed Hadj Kacem . . . . . . . . . . . . . . .642 Adaptive Security for Cloud Data Warehouse as a Service Emna Guermazi, Mounir Ben Ayed, Hanêne Ben-Abdallah. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 Enriching User Model Ontology for Handicraft domain by FOAF Maha Maalej, Achraf Mtibaa, Faïez Gargouri. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651 Integrating semantics and structural information for BPMN model refactoring Wiem Khlif, Hanêne Ben-Abdallah. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 Context Aware Criteria For The Evaluation Of Mobile Decision Support Systems Emna Ben Ayed, Mounir Ben Ayed, Christophe Kolski, Houcine Ezzedine, Faiez Gargour. . . . . . . . . . . . . . . . 661 Ensemble Feature Selection of microRNAs and Human Cancer Classifications Minghao Piao, Hyoung Woon Song, Keun Ho Ryu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Author Index 673 Ensemble Feature Selection of microRNAs and Human Cancer Classification Minghao Piao1, Hyoung Woon Song2, Keun Ho Ryu* 1,* Database/Bio informatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University Cheongju, South Korea 2 Plant Engineering Center/Clean Energy Team, Institute for Advanced Engineering, Yongin, Korea {1bluemhp, *khryu}@dblab.chungbuk.ac.kr, [email protected] * Abstract—For the selection of most significant microRNAs and its use in human cancer classification, traditional feature selection methods are widely used like filter approach, embedded approach and wrapper approach. However, some studies report that these methods would decrease the stability of biomarkers. Recently, ensemble feature selection methods are very popular in bioinformatics to improve the stability of biomarkers. In our study, we describe a data diversity ensemble based feature selection method for microRNAs based human cancer classification. The results show that our approach can select most significant microRNAs with high quality of classification. Keywords—Ensemble feature selection, Cascading-andSharing, data diversity, microRNAs, Human cancer classification I. INTRODUCTION Many data mining techniques [1-3] have been applied to the microRNA expression data for human cancer classification since a class of small non-coding RNAs have been proved that the abnormal expression data can indicate human cancer [4, 5]. However, there are still several issues [6] have to be solved: (1) Curse of dimensionality: due to the large number of genes, it is difficult to focus on informative genes. The high dimensionality may cause a series of problems for cancer classification, such as add noise, reduce the accuracy rate, and increase the complexity. (2) Choosing the most appropriate small number of genes is extremely difficult. To solve these problems, we can use feature selection or feature extraction methods to reduce the dimensionalities. Feature selection is better than feature extraction for microRNA expression data analysis because feature selection methods simply choose number of appropriate microRNAs and it can preserve original characteristics of microRNAs, whereas feature extraction is aimed to create new features using some transform functions of the original microRNAs, but these new features maybe not able to explain the physical aspect. In Lu et al.’s work [7], they used bead-based flow cytometric microRNA expression profiling method to analyze the 217 mammalian microRNAs from 334 samples including * Corresponding author 978-1-4799-8679-8/15/$31.00 copyright 2015 IEEE ICIS 2015, June 28-July 1 2015, Las Vegas, USA human cancers. And the result showed the potential of microRNA profiling in cancer diagnosis. Based on this data resource, many works using different feature selection methods and classification methods have been conducted to do the cancer classification [8, 9, 10, 34, 35]. For most of feature selection methods, it is difficult to define appropriate number of high-ranked microRNAs. In such case, the most widely used approach is to define the appropriate number of microRNAs by comparing the classification performance of different number of high-ranked microRNAs which gives better performance. However, such kind of approach is not useful when there are numerous high-ranked microRNAs or the calculated measurement shows linear changes which makes difficulty to define high-ranked microRNAs. For improving the stability of feature selection methods in bioinformatics, researchers have proposed new frameworks such as ensemble feature selection methods. In this study, we propose a data diversity ensemble based feature selection method TOp-K Significant features from Cross VAlidation (TOSCVA). The advantage of the proposed method is that it can effectively solve the singleton and fragmentation problem in classification. The experimental results show that our method can find most significant features and it is suitable for its intended use in classification. II. ENSEMBEL FEATUR SELECTION When looking for biomarkers from DNA, RNA and microRNAs expression data, only a small subset of biomarkers are selected which are related to specific diseases. One of the most common approaches is through ordering or ranking the genes by their importance. Ordering genes by their importance is very similar to feature selection which is a preprocessing step of data mining. The feature selection methods can return a set of features that are most important to the problem at hand. Feature selection methods can be applied to several issues in biology and genetics: distinguishing between healthy and diseased tissue [11, 12, 13, 33]; identification and classification of different types of cancer [14, 15]; prediction of drug treatment [16, 17], etc. In bioinformatics, data sets usually contain few samples (often less than a hundred) and thousands of different genes (curse of dimensionality). This will decrease the stability of feature rankers and lead to generating different results after slightly changing the data set [18]. In order to improve the stability of feature selection techniques, researchers have proposed new frameworks such as ensemble feature selection methods [19-25]. The idea of ensemble feature selection is derived from ensemble learning methods wherein different classifiers are applied to a dataset and their results are aggregated. Ensemble feature selection techniques apply feature selection algorithms multiple times and combine the results into the decision making. Because combining multiple results, the features which are frequently chosen as the best performers will be marked as top-ranked features, while features with poor performance will be lowranked features; thus, the final top-ranked features will be more stable. There are three main types of ensemble feature selection techniques [26]. (1) Data diversity consists of applying a single feature selection method to a number of differently sampled versions of the same dataset and then an aggregation technique is used to aggregate the results. (2) Functional diversity is performed by applying a set of different feature selection techniques on the same dataset. (3) Hybrid ensembles use both of these, applying different feature selection techniques to different sampled versions. III. DECISION TREE ENSEMBLE BASED FEATURE SELECTION A. Problem Definition In data mining, ensemble methods are used for improving the classifier’s accuracy. Ensemble methods are used to construct a set of base classifiers from training data set and perform the classification work by voting on the predictions made by each classifier. Since the idea of ensemble feature selection is derived from ensemble learning methods, it is possible to apply ensemble learning methods in feature selection if the method can decide which features to construct the set of classifiers. The ensemble of classifiers can be constructed in many ways [27] and most widely used is by manipulating the training set like bagging and boosting. Three interesting observations are described in [28] based on the study of many ensemble methods: (1) Many ensembles constructed by the Boosting method were singletons. Due to this constraint, deriving classification rules have a limitation: decision trees are not encouraged to derive many significant rules and they are mutually exclusive and covering the entire of training samples exactly only once. (2) Many top-ranked features possess similar discriminating merits with little difference for classification. This indicates that it is worthwhile to employ different top-ranked features as the root nodes for building multiple decision trees. (3) Fragmentation problem is another problem that those ensemble methods have: as less and less training data are used to search for root nodes of sub-trees. Based on those observations, if we want to apply ensemble learning method to feature selection in bioinformatics like selecting most useful microRNAs, we need a method that can break the singleton coverage constraint and solve the fragmentation problem. Our previous study [34] has mentioned that microRNAs selected from traditional feature selection methods are not the most top-ranked features. Therefore, in our study, we are going to introduce a method that can produce most top-ranked features. B. TOSCVA Decision tree is commonly used in classification for the purpose of decision making. Decision tree is attractive for 3 reasons: (1) Decision tree is a good generalization for unobserved instance, only if the instances are described in terms of features that are correlated with the target concept. (2) The methods are efficient in computation that is proportional to the number of observed training instances. (3) The result of decision tree provides a representation of the concept that is explainable to humans. Also, decision tree could be used as a feature selection method since the algorithm itself decides which features to construct the tree structure. Bagging and boosting are first approach they construct multiple base trees, each time using a bootstrapped replication of the original training data. Bagging [30] is a method for generating multiple decision trees and using these trees to get an aggregated predictor. The multiple decision trees are formed by bootstrap aggregating which repeatedly samples from a data set and the sampling is done with replacement. It is that some instances may appear several times in the same training set, while others may be omitted from the training set. Unlike bagging, boosting [31] assigns a weight to each training example and may adaptively change the weight at the end of each boosting round. However, it is impossible to select most significant features since multiple decision trees have chance to use different set of features. In this study, we propose an ensemble feature selection method named TOp-K Significant features from Cross VAlidation (TOSCVA) in order to efficiently select most significant features. TOSCVA consists of three phases: at first, by using the mechanism of cross validation, it creates different K training data sets according to user given parameter K; second, according to given parameter N, N number of decision trees is constructed from each K training data sets. The parameter N determines the number of top ranked features which will be forced to be the root of a decision tree [28, 29]; finally, the distinguishing power of K decision tree committees is evaluated to detect final N most significant top ranked features. Input K: number of training data set N: number of top ranked features M: denotes given data set Output N number of significant features Data_sampling(K,M); { Produce K equal training data sets from M; Return TrainingData[K]; } Ensemble_Feature_Selection(TrainingData[K],N) { For each TrainingData[K] { Best N features are selected; Tree_Construction() {force each best feature to be the root;} Significance_Evaluation() {the significance of each feature is evaluated;} } Return most significant N number of features; } Algorithm 1. The skeleton of TOSCVA considering too many features during the process. In other words, we are looking for most smaller number of features with highest performance. From Table 2 and Table 3, we can see that the performances on 55 ~ 64 and 73 ~ 80 microRNAs are same. Therefore, it is better to choose 55 top-ranked microRNAs as most significant microRNAs in human cancer classification. Figure 3 shows the running time cost of different number of top-ranked features during three times of tests. Since we have to build the tree committee based on the number of topranked features, the running time cost increases when the number of top-ranked features are increasing. IV. EXPERIMENTAL RESULTS A. microRNA Dataset The microRNA expression dataset was first published by [32]. They used a bead-based method to present a systemic expression analysis of 217 mammalian microRNAs from 186 samples including multiple human cancers. The used data set in this paper is described in Table 1. TABLE I. THE NUMBER OF THE SAMPLES FOR EACH CANCER TYPE Cancer Name Colon Pancreas Uterus Mesothelioma Breast B Cell ALL T Cell ALL Follicular Cleaved Lymphoma Large B Cell Lymphoma SUM Fig. 1. Classification accuracy on different number of top-ranked microRNAs. No. of Tumor Samples 10 9 10 8 6 26 18 8 8 103 Fig. 2. Classification accuracy on 50~80 top-ranked microRNAs. B. Feature Selection and Classification Figure 1 shows the classification accuracy of our approach on the 10 ~ 217 microRNAs with interval of 10. When the number of given top-ranked microRNAs are in the interval of 10 ~ 60, the accuracy is increasing with bigger number of microRNAs. And, the classifier shows the highest accuracy when the given number of microRNAs is 60 and 80. Therefore, we are going to choose top-ranked microRNAs as most significant microRNAs in the interval of 50 ~ 80. Figure 2 shows the classification accuracy of the method on the 50 ~ 80 microRNAs. We can see that the classifier shows highest accuracy when the number of given microRNAs are in the interval of 55 ~ 64 and 73 ~ 80. When the given number of top-ranked microRNAs is 65 ~ 72, the accuracy is decreased. It means that there are some microRNAs which are not suitable to build decision tree committees when the number is bigger than 64 even it shows highest accuracy in the interval of 73 ~ 80. Also, the cost of decision tree induction will become expensive when Fig. 3. Running time cost of different number of top-ranked features. After checking the microRNAs that exactly used to construct decision trees, we have found most common microRNAs that often used in decision tree induction which are independent from given number of top-ranked microRNAs. - no-miR151*:UCGAGGAGCUCACAGUCUAGUA:bead_160-C - hsa-miR-125b:UCCCUGAGACCCUAACUUGUGA:bead_102-A - hsa-miR-18:UAAGGUGCAUCUAGUGCAGAUA:bead_129-A - mmu-miR-155:UUAAUGCUAAUUGUGAUAGGGG:bead_126-A - hsa-miR-10a:UACCCUGUAGAUCCGAAUUUGUG:bead_120-A TABLE II. No. microRNAs 55 ~ 64 DETAILED CLASSIFICATION PERFORMANCE Classes (No. of instances) Precision 0.778 0.7 0.737 Pancreas (9) 0.778 0.778 0.778 Uterus (10) 0.7 0.7 0.7 Mesothelioma (8) 0.833 0.625 0.714 Breast (6) 0.75 1 0.857 B Cell ALL (26) 0.926 0.962 0.943 T Cell ALL (18) 0.947 1 0.973 Follicular Cleaved Lymphoma (8) 0.714 0.625 0.667 Large B Cell Lymphoma (8) 0.625 0.625 0.625 Colon (10) 0.778 0.7 0.737 Pancreas (9) 0.778 0.778 0.778 Uterus (10) 0.7 0.7 0.7 0.833 0.625 0.714 0.857 Breast (6) 0.75 1 B Cell ALL (26) 0.926 0.962 0.943 T Cell ALL (18) 0.947 1 0.973 Follicular Cleaved Lymphoma (8) 0.714 0.625 0.667 Large B Cell Lymphoma (8) 0.625 0.625 0.625 TABLE III. No. microRNAs 55 ~ 64 73 ~ 80 F-measure Colon (10) Mesothelioma (8) 73 ~ 80 Recall CONFUSION MATRIX Classes (classified as) a b c d e f g h i Colon (a) 7 1 2 0 0 0 0 0 0 Pancreas(b) 1 7 1 0 0 0 0 0 0 Uterus(c) 1 1 7 1 0 0 0 0 0 Mesothelioma(d) 0 0 0 5 0 2 0 1 0 Breast(e) 0 0 0 0 6 0 0 0 0 B Cell ALL(f) 0 0 0 0 0 25 1 0 0 T Cell ALL(g) 0 0 0 0 0 0 18 0 0 Follicular Cleaved Lymphoma(h) 0 0 0 0 0 0 0 5 3 Large B Cell Lymphoma(i) 0 0 0 0 2 0 0 1 5 Colon (a) 7 1 2 0 0 0 0 0 0 Pancreas(b) 1 7 1 0 0 0 0 0 0 Uterus(c) 1 1 7 1 0 0 0 0 0 Mesothelioma(d) 0 0 0 5 0 2 0 1 0 Breast(e) 0 0 0 0 6 0 0 0 0 B Cell ALL(f) 0 0 0 0 0 25 1 0 0 T Cell ALL(g) 0 0 0 0 0 0 18 0 0 Follicular Cleaved Lymphoma(h) 0 0 0 0 0 0 0 5 3 Large B Cell Lymphoma(i) 0 0 0 0 2 0 0 1 5 V. CONCLUSION In data mining, traditional feature selection methods can be divided into filter approach, embedded approach and wrapper approach. In bioinformatics, the weakness of these methods is that they would decrease the stability of biomarkers. Recently, ensemble feature selection methods are very popular in bioinformatics to improve the stability of biomarkers. The ensemble feature selection methods can be divided into three types: Data diversity, Functional diversity and Hybrid ensemble. However, there are still no studies about application of ensemble feature selection in microRNAs based human cancer classification. In our study, we described an ensemble feature selection method TOSCVA for microRNAs and human cancer classification. The experimental results show that our approach is useful to define most significant microRNAs by evaluating the classification performance of top-ranked features. Also, we have found several most common microRNAs which are most often used in decision tree induction with different number of top-ranked. Based on the experimental results, we believe that our method can be used in various research areas which needs to solve the curse of dimensionality problem. Also, by using different feature ranking methods, our method can produce different set of top-ranked features. It indicates that our method has capability to be used in different data sets which have different characteristics. Our future work will be focusing on the application of Functional diversity and Hybrid ensemble method on microRNAs and trying to design new ensemble feature selection method by considering different feature ranking methods in our mechanism. [5] [6] [7] [8] [9] [10] [11] [12] [13] Acknowledgment This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (2014-H0301-14-1022) supervised by the NIPA (National IT Industry Promotion Agency), and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.2013R1A2A2A01068923), and by Export Promotion Technology Development Program, Ministry of Agriculture, Food and Rural Affairs (No.114083-3). [14] [15] [16] [17] References [1] [2] [3] [4] X. Wang, Robust two-gene classifiers for cancer prediction, Genomics, 2011, pp. 90-95. L. Li, W. Jiang, X. Li, K.L. Moser, Z. Guo, L. Du, Q. Wang, E.J. Topol, Q. Wang, S. Rao, A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset, Genomics, 2005, pp. 16-23. T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, Y. Saeys, Robust biomarker identification for cancer diagnosis with ensemble feature selection methods, Bioinformatics, 2010, pp. 392-398. L. He, J. M. Thomson, M. T. Hemann, E. Hernando-Monge, D. Mu, S. Goodson, Powers S, Cordon-Cardo C, Lowe SW, Hannon GJ, [18] [19] [20] Hammond SM, A microRNA polycistron as a potential human oncogene, Nature, 2005, 435, pp. 828-833. M. Mraz, S. Pospisilova, K. Malinova, I. Slapak, J. Mayer, MicroRNAs in chronic lymphocytic leukemia pathogenesis and disease subtypes, Leuk Lymphoma, 2009, pp. 506-509. N. Rosenfeld, R. Aharonov, E. Meiri, S. Rosenwald, Y. Spector, M. Zepeniuk, H. Benjamin, N. Shabes, S. Tabak, A. Levy, MicroRNAs accurately identify cancer tissue origin, Nat. Biotechnol, 2008, pp. 462469. J. Lu, G. Getz, E. A. Miska, E. Alvarez-Saavedra, J. Lamb, D. Peck, A. Sweet-Cordero, B. L. Ebert, R. H. Mak, A. A. Ferrando, J. R. Downing, T. Jacks, H. R. Horvitz, T. R. Golub, MicroRNA expression profiles classify human cancers, Nature, 2005, 435, pp. 834-838. N. Rosenfeld, R. Aharonov, E. Meiri, S. Rosenwald, Y. Spector, M. Zepeniuk, H. Benjamin, N. Shabes, S. Tabak, A. Levy, D. Lebanony, Y. Goren, E. Silberschein, N. Targan, A. Ben-Ari, S. Gilad, N. Sion-Vardy, A. Tobar, M. Feinmesser, O. Kharenko, O. Nativ, D. Nass, M. Perelman, A. Yosepovich, B. Shalmon, S. Polak-Charcon, E. Fridman, A. Avniel, I. Bentwich, Z. Bentwich, D. Cohen, A. Chajut, I. Barshack, MicroRNAs accurately identify cancer tissue origin, Nat Biotechnol, 2008, 26, pp. 462-469. R. Xu, J. Xu, D. C. Wunsch II, MicroRNA expression profile based cancer classification using Default ARTMAP, Neural Networks, 2009, 22, pp. 774-780. Kyung-Joong Kim, Sung-Bae Cho, Exploring features and classifiers to classify microRNA ex-pression profiles of human cancer, Neural Information Processing, 2010, 6444, pp. 234-241. U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine, “Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays,” Proceedings of the National Academy of Sciences, 1999, vol. 96, no. 12, pp. 6745-6750. S. Dudoit, J. Fridlyand, and T. P. Speed, Comparison of discrimination methods for the classifi-cation of tumors using gene expression data, Journal of the American Statistical Association, 2002, vol. 97, no. 457, pp. 77-87. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, 2002, vol. 46, pp. 389-422. A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. Schummer, and Z. Yakhini, Tissue classification with gene expression profiles, Journal of Computational Biology, 2000, vol. 7, no. 3-4, pp. 559-583. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander, Molecular classification of cancer:Class discovery and class prediction by gene expression monitoring, Science, 1999, vol. 286, no. 5439, pp. 531-537. D. Dittman, T. Khoshgoftaar, R. Wald, and A. Napolitano, “Random forest: A reliable tool for patient response prediction,” Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Workshops. BIBM, 2011, pp. 289-296. G. Mulligan, C. Mitsiades, B. Bryant, F. Zhan, W. J. Chng, S. Roels, E. Koenig, A. Fergus, Y. Huang, P. Richardson, W. L. Trepicchio, A. Broyl, P. Sonneveld, J. Shaughnessy, John D., P. Leif Bergsagel, D. Schenkein, D.-L. Esseltine, A. Boral, and K. C. Anderson, Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib, Blood, 2007, pp. 3177-3188. A. Kalousis, J. Prados, and M. Hilario, Stability of feature selection algorithms: a study on high-dimensional spaces, Knowledge and Information Systems, 2006, vol. 12, no. 1, pp. 95-116. T. Abeel, T. Helleputte, Y. Van de Peer, P. Dupont, and Y. Saeys, Robust biomarker identification for cancer diagnosis with ensemble feature selection methods, Bioinformatics, 2010, vol. 26, no. 3, pp. 392398. A. C. Haury, P. Gestraud, and J. P. Vert, The influence of feature [21] [22] [23] [24] [25] [26] [27] [28] [29] selection methods on accuracy, stability and interpretability of molecular signatures, PLoS ONE, 2011, vol. 6, no. 12, pp. e28210. H. Liu, L. Liu, and H. Zhang, Ensemble gene selection by grouping for microarray data classification, Journal of Biomedical Informatics, 2010, vol. 43, no. 1, pp. 81-87. Y. Saeys, T. Abeel, and Y. Peer, “Robust feature selection using ensemble feature selection techniques,” Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II. Berlin, Heidelberg: Springer-Verlag, 2008, pp.313325. P. Yang, J. Ho, Y. Yang, and B. Zhou, Gene-gene interaction filtering with ensemble of filters, BMC Bioinformatics, 2011, vol. 12, no. Suppl 1, pp. S10. P. Yang, Y. Hwa Yang, B. B Zhou, and A. Y Zomaya, A review of ensemble methods in bioinformatics, Current Bioinformatics, 2010, vol. 5, no. 4, pp. 296–308. L. Yu, Y. Han, and M. E. Berens, Stable gene selection from microarray data via sample weighting, IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2012, vol. 9, no. 1, pp. 262–272. Awada, Wael, et al. “A review of the stability of feature selection techniques for bioinformatics data”, 2012 IEEE 13th International Conference on Information Reuse and Integration (IRI), 2012, pp. 356363. P. N. Tan, M. Steinbach, V. Kumar, Ensemble methods. Introduction to data mining, Addision Wesley, 2006, pp. 278-280. J. Y. Li, H. A. Liu, See-Kiong Ng, Limsoon Wong, Discovery of significant rules for classifying cancer diagnosis data, Bioinformatics, 2003, vol. 19, pp. 93-102. J. Li, H. Liu, “Ensembles of cascading trees”, Proceedings of Third IEEE international conference on data mining, 2003, 585-588. [30] L. Breiman, Bagging predictors, Machine Learning, 1996, vol. 24, pp. 123-140. [31] Y. Freund, R. E. Schapire, “Experiments with a New Boosting Algorithm”, Proceedings of the Thirteenth International Conference on Machine Learning, 1996, pp. 148-156. [32] E. Fridman, Z. Dotan, I. Barshack, M.B. David, A. Dov, S. Tabak, O. Zion, S. Benjamin, H. Ben-jamin, H. Kuker, Accurate molecular classification of renal tumors using microRNA expression, The Journal of molecular diagnostics, 2010, pp. 687-696. [33] Y. J. Piao, H. W. Park, C. H. Jin and K, H. Ryu, “Ensemble Method for Classification of High Dimensional Data”, Proceedings of the International Conference on Big Data and Smart Computing, 2014, pp. 245-249. [34] F. F. Li, Y. J. Piao, M. J. Li, M. H. Piao and K, H. Ryu, “Positive Impression of Low-Ranking microRNAs in Human Cancer Classification”, Proceedings of The Third International conference on Parallel, Distributed Computing technologies and Applications (PDCTA-2014), 2014. [35] Y. J. Piao, N. H. Choi, M. J. Li, M. H. Piao and K, H. Ryu, “Ensemble Method for Prediction of Prostate Cancer from RNA-Seq Data”, 6th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2014, pp. 51-56.