Download Artificial Intelligence Final Project

Artificial Intelligence Final Project
Text document Classification
with new type Rule-based PLM
Chang, Jung Woo
Shin, Dong In
Jung, Hyun Joon
School of Computer Science and Engineering
Seoul National University
Presented by Jung Hyun Joon
2004. 12. 21
Artificial Intelligence Final Project
Contents
•
•
•
•
•
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Introduction
Architecture
Wet design scheme
Performance Evaluation
Conclusion
References
Artificial Intelligence Final Project
Introduction
• Classification Problem
– Decision Tree & Version space learning ..
– Some shortcomings
• Not include all possible rule sets, only focus part..
• Vulnerable to noisy data
• In this paper
– Utilize massive parallelism of DNA computing
– Define rules as a element with 1 / 0 / don’t care
– Make noise-tolerant classification system
Artificial Intelligence Final Project
Architecture
• Rule-based PLM
– training and test
Rule-based PLM의 전체적인 구조
Artificial Intelligence Final Project
Target Data and Model Structure
Property of target data
Document i
1
0
1
Can be involved in
class A, B or C
0
Model structure
Class Tag + n digit binary bit + history count in Training
A
1
0
1
0
13
B
1
0
1
0
22
C
1
0
1
0
07
Artificial Intelligence Final Project
Training
Training query
A
B
0
1
0
0
1
0
A
0
0
0
0
0
A
0
0
0
0
0
B
0
0
0
0
0
B
0
0
0
0
0
1
…
…
A
1
0
1
0
0
A
1
0
1
0
1
2
B
1
0
1
0
0
B
1
0
1
0
0
1
C
1
0
1
0
0
C
1
0
1
0
0
*
*
0
…
C
*
*
…
*
*
0
C
*
*
Artificial Intelligence Final Project
Test
Test query
A
1
0
1
0
A
0
0
0
0
4
B
0
0
0
0
3
Class A – 12 / 17
Class B – 2 / 17
Class C – 3 / 17
Class A
…
A
1
0
1
0
12
B
1
0
1
0
2
C
1
0
1
0
3
*
*
98
…
C
*
*
Artificial Intelligence Final Project
Wet-design Scheme
Initial DNA strand 생성 과정
Artificial Intelligence Final Project
Wet design Scheme
training example set 생성 과정
Artificial Intelligence Final Project
Wet design Scheme
classification 과정
Artificial Intelligence Final Project
Forward and Backward scheme to untrained query
Comparison of the forward and backward
model scheme
Artificial Intelligence Final Project
Performance Evaluation.
Artificial Intelligence Final Project
Performance Evaluation
Average Classification Success Rate
CISI classification Success Rate
Artificial Intelligence Final Project
Performance Evaluation
CRAN Classification Success Rate
MED Classification Success Rate
Cause of MED classification success rate
1. Preprocessing ( all zero term document delete )
2. Sparse vector of term
Artificial Intelligence Final Project
Conclusion
• Present new type rule-based PLM
– Support the flexibility with don’t care property
– Forward and backward search scheme to untrained
query
– Showing the similar performances compared with
WEKA
– Possibility of wet-design
Artificial Intelligence Final Project
References
• Version Space Learning with DNA Molecules, Lim, H.-W.
et al, LNCS, vol. 2568, pp. 143-155, 2003
• DNA computing on surfaces, Liu et al., Nature, 2000
• A Bayesian Algorithm for In Vitro Molecular Evolution of
Pattern Classifiers, Zhang, B.-T. and Jang, H.-Y.,
Preliminary Proceedings of the Tenth International
Meeting on DNA Computing, pp. 294-303, 2004
• 10 more papers and many web-sites