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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 6 ISSUE 1 – JUNE 2015 - ISSN: 2349 - 9303 Efficiency Improvement in Classification Tasks using Naive Bayes Tree and Fuzzy Logic Revathi.K1 1 2 K.S.RInstituteforEngineeringandTechnology, ComputerScienceand Engineering, [email protected] Jawahar.M2 K.S.RInstituteforEngineeringandTechnology, ComputerScienceand Engineering, [email protected] Abstract—ForImproving the classifications accuracy rates for Naive Bayes tree (NBTREE) and Fuzzy Logic for the classification problem. In our first proposed NBTREE algorithm, due to presence of noisy inconsistency instances in the training set its may because Naïve Bayes classifiers tree suffers from over fittings its decrease accuracy rates then we have to compute Naïve Bayes tree algorithm (NBTREE)to remove the unwanted noisy data from a large amount of training dataset. Then our second the proposed fuzzy logic algorithm, we apply Naïve Bayes tree (NBTREE) to select alsoa more important subset of features for the production of Naive assumption of class conditional independence, to improve extract valuable training dataset and we verified the performances of the two proposed algorithm against those the existing systems are Naïve Bayes tree induction and Fuzzy logic classification individually using the classification accuracy validation. Thus result may cause that identity the most sufficient attributes for the explanation of instances and accuracy rates has to be improved. Index Terms— Classification, Naïve Bayes tree (NBTree), Fuzzy Logic, Decision tree induction, Naïve Bayes Classifiers, Preprocessing. 1 INTRODUCTION Classification is an important task in data mining .Currently, classification as have huge training data set are available, and thena big interest for developing classifiers that allow the handlingkind of datasets in a reasonable time. There is another technique for reducing the number of attributes used in a tree –pruning. Two types of pruning: a. b. a. Pre-pruning (forward pruning) b. Post-pruning (backward pruning) 2.3 LEVENT et.al (2011)The Naïve Bayes method, which is the simplest form of a Bayesian network, is a popular data mining method that has been applied to many domains, including intrusion detection. The method’s simplicity trusts on the assumption that all of the features are independent of each other. The HNB method, which eases this assumption, has stayedeffectively applied to web mining. Post -pruning waits until the full decision tree has built and then prunes the attributes. Two techniques: a. Sub tree Replacement b. Sub tree Raising 2.2 LEE et.al (2010)A Naïve Bayes classification enhancing technique. The development is seen in terms of an improvement in the classification accuracy and is realized through applying unique weighting factors to each category based on the number of documents that are annotated to them. The results from our experiments show that the weighting factor capacityis presented and described as has improved a classification accuracy of the ordinary Naïve Bayes classification method. Pre -pruning, we decide during the building process when to stop adding attributes (possibly based on their information gain) 2 RELATED WORKS 2.1 Chandra and Varghese (2009)The G-FDT tree used the Gini Index as the split measure to choose the most appropriate splitting attribute for each node in the decision tree. Inspired by performance and unambiguousnessreflections, we propose a new node intense measure in this paper ,we show that the proposed measure is convex and well behaved. Our results over a large number of problems indicate that the quantity results in smaller trees in a large quantity of the cases without any loss in classification accuracy. 3 EXISTING SYSYTEM Hybrid mining algorithms for improve the classifications accuracy rates of decision tree (DT)and Naive Bayes (NB) classifiers for the classification of multi-class problems in medical dataset. Naïve Bayes (NB) and Decision Tree (DT) classifiers for the automatic analysis and classification of attribute data from training course web pages. I. Decision tree induction II. Naive Bayes classification 167 INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 6 ISSUE 1 – JUNE 2015 - ISSN: 2349 - 9303 greedy approach, which offers a rapid and effective method for classifying data instances.Decision tree is based onflow chart-like tree structure and internal node denotes a test on an attributewhich Branch represents an outcome of the test and Leaf nodes represent class labels or class distribution. I. Decision tree generation consists of two phases Tree construction At start, all the training examples are at the root Partition examples recursively based on selected attributes Tree pruning Identify and remove branches that reflect noise or outliers II. Use of decision tree: Classifying an unknown sample Test the attribute values of the sample against the decision tree Decision tree is a classifier in the form of a tree structure. • Decision node: specifies a test on a single attribute • Leaf node: indicates the value of the target attribute • Arc/edge: split of one attribute • Path: a disjunction of test to make the final decision 3.2 NAÏVE BAYES CLASSIFICATION A naive Bayes classifier is a simple probabilistic based method, which can predict the class membership .It has several advantages: (a) easy to use (b) only one scan of the training data required for probability generation. Bayesian belief network allows a subset of the variables conditionally independentand agraphical model of causal relationships. Several cases of learning Bayesian belief networks a. Given both network structure and all the variables: easy b. Given the network structure, but only some variables: use gradient descent / EM algorithms c. When the network structure is not known in advance i. Learning structure of network harder 3.1 DECISION TREE INDUCTION The decision tree classifier is typically a top-down Naïve Bayes tree(NB TREE) Fuzzy logic 4.1 Naïve Bayes tree (NB TREE):The NBTREE algorithm is similar to the classical recursive partitioning schemes, not including that the leaf nodes created are naive Bayes categorizers with reverse of nodes predicting a single class. It attempts to approximate whether the generalization accuracy for a naïveBayes classifier at the current node. • Entropy • Residual information • Information Gain Ratio • Gini Index 4.2 Fuzzy logic (FL):Fuzzy logic is a logical system. FL is approach to computing based system its mainly contains true or false methods. Fuzzy logic reduces the design steps and reduced complexity. An accurate quantitative model is not required to control appropriate action. Support system tool in fuzzy logic. Operations on Classical Sets Union: HI = {x | x H or x I}. Intersection: HI= {x | x H and x I}. Complement: c H = {x | x H, x H}. H = universe of discourse = the set of all objects with the same characteristics. Let nx = cardinality = total number of elements in H. For crisp sets H and I in H, we define: xH x belongs to H. x H x does not belong to H. For sets A and B on X: H IxH, xI. H I H is fully contained in I. H = I H I and I H. The null set, , contains no elements. 5 EXPERMENTIAL SETUP: Where, P(x) P(x/ci) evaluating P(ci) P(ci/x) = Constant for all classes = Order to reduce computation in To test the proposedsystem hybrid methods, we have used the classification accuracy and 10- fold cross validation. To improve the classification accuracy rates for Naive Bayes tree (NBTREE) and Fuzzy Logic for multi class problem. = Class prior probabilities = Maximum Posteriori Hypothesis Equation 1 accuracy rates 4 PROSPOSED WORK: Propose a new method to improve the classification accuracy ratesusinga Naïve Bayes tree(NBTREE) and fuzzy logic. 168 INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 6 ISSUE 1 – JUNE 2015 - ISSN: 2349 - 9303 5.1 ANALYZING THE DATA SET In our project we get a dataset from .DAT file as our file reader program will get the data from them for the input of Naïve Bayes based mining process. NB models are popular in machine learning applications, due to their simplicity in allowing each attribute to contribute towards the final decision equally and independently from the other attributes. This simplicity equates to computational efficiency, which makes NB techniques attractive and suitable for many domains. FIGURE 5.2 EXISTING ALGORTHIM 5.3 NAIVES BAYES IMPLEMENTATION IN MINING: Bayes' Theorem finds the probability of an event occurring given the probability of another event that has already occurred. If X represents the dependent event and Y produces the prior event, Bayes' theorem can be certain as follows: • • • • • Responses are analyzed Depending upon the responses graph the structure is resulted Using Bayes theorem techniques Naïve Bayes classifier is implemented Accuracy rate is calculated Comparative analyses on the obtained result is done further 5.4 COMPARISON OF ACCURACY RATES The obtained results for patient are compared with the already existing results and the accuracy is calculated. Figure 5.1 Naive Bayes Classifiers 6 RESULTS: 5.2 DESIGNING THE INPUT ATTRIBUTES Forms have advantages over some other types of medical symptoms that they are reasonable, do not lack as much effort from the questioner as verbal or telephone surveys, and generally have standardized answers that make it simple to compile data. However, such standardized answers may confront users. Forms are also clearly limited by the fact that respondents must be able to read the questions and respond to them. • • • • 6.1 NAÏVE BAYES TREE The classification accuracies for a Naïve Bayes Tree with 6 –fold cross validation. Formsare based on the attributes given in the dataset Each attribute has separate questions in terms of values Questions are given to the respondents a simple way with higher understanding Analyses is done after enrollment of the answers 169 Table 6.1: Naïve Bayes Tree INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 6 ISSUE 1 – JUNE 2015 - ISSN: 2349 - 9303 REFERNCES: Figure 6.1: Graph of Naïve Bayes Tree 6.2 FUZZY LOGIC: The classification accuracies values for a Fuzzy Logic with 6 –fold cross validation. 1. Aitken head, M. J. (2008) ‘A co-evolving decision tree classification method’ Expert Systems with Applications. 2. Aviad, B. & Roy G. (2011) ‘Classification by clustering decision tree-like classifier’ Expert Systems with Applications. 3. Balamurugan, S.A.A. &Raja ram, R. (2009) ‘Effective solution for unhandled exception in decision tree induction algorithms’. Expert Systems with Applications, 12113–12119. 4. Breiman, L. Friedman, J. Stone, C. J. &Olshen, R. A. (1984) ‘Classification and regression trees. 5. Bujlow, T. Riaz, (2012) ‘A method for classification of network traffic established on C5.0 machine learning algorithm’. (pp. 237–241). 6. Chandra, B. & Gupta, M. (2011) ‘Robust approach for estimating probabilities in naive Bayesian classifier for gene expression data’. Expert Systems with Applications, (pp.1293–1298). 7. Chandra, B., & Paul Varghese, P. (2009) ‘Moving towards efficient decision tree construction’. Information Sciences, 179, 1059–10 8. Chandra, B. & Varghese, P. P. (2009) ‘Fuzzifyinggini index based decision trees’. Expert Systems with Applications, 36, 8549–8559. 9. Fan, L. Poh, K.-L. & Zhou, P. (2010) ‘Partitionconditional ICA for Bayesian classification of microarray data’. Expert Systems with Applications, 37,8188–8192. Table 6.2: Fuzzy Logic 10. Franco-Arcega, A.Carrasco-Ochoa, J.A. Sanchez-Diaz, G. & Martinez-Trinidad, J. F. (2011) ‘Decision tree induction using a fast splitting attribute selection for large datasets’. Expert Systems with Applications, 38, 14290–14300. 11. Hsu, C.-C., Huang, Y.-P & Chang, K.-W. (2008) ‘Extended naive Bayes classifier formixed data’. Expert Systems with Applications, 35, 1080–1083. 12. Koc, L. Mazzuchi, T. A &Sarkani, S. (2012). ‘A network intrusion detection system based on a hidden naive Bayes classifier’. Expert Systems with Applications, 42, 13491– 13500. Figure 6.2 : Graph of Fuzzy Logic 7 CONCLUSIONS: In this, new techniques called a naïve Bayes tree (NBTREE) and fuzzy logic is used for any type of dataset to display the most accuracy rates for dataset. The NBTREE and fuzzy logic will makes preprocessing and the pruning, tree more accuracy than the naïve Bayes classifiers and decision tree induction. The comparison of two datasets will be more accurate than of the existing system. In this, study is made about domain information and also the literature surveys is conducted in this the area of classification techniques and algorithm. The design of the proposed system is prepared to solve the problem in the existing system. 170 13. Lee, L. H. & Isa, D. (2010). ‘Automatically computed document dependent weighting factor facility for naıve Bayes classification’. Expert Systems with Applications, 37, 8471–8478.