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Efficiency Improvement in Classification Tasks using Naive Bayes Tree and Fuzzy Logic
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.
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL