Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier

2004 ◽  
Vol 44 (1) ◽  
pp. 170-178 ◽  
Author(s):  
Andreas Bender ◽  
Hamse Y. Mussa ◽  
Robert C. Glen ◽  
Stephan Reiling
Keyword(s):  
Feature Selection ◽  
Molecular Similarity ◽  
Bayesian Classifier ◽  
Similarity Searching ◽  
Naïve Bayesian Classifier ◽  
Naive Bayesian ◽  
Naive Bayesian Classifier ◽  
Naïve Bayesian

scholarly journals A Hybrid Feature Selection Method Based on Rough Conditional Mutual Information and Naive Bayesian Classifier

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zilin Zeng ◽  
Hongjun Zhang ◽  
Rui Zhang ◽  
Youliang Zhang
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Feature Selection Method ◽  
Bayesian Classifier ◽  
Feature Subset ◽  
Conditional Mutual Information ◽  
Naïve Bayesian Classifier ◽  
Naive Bayesian ◽  
Naive Bayesian Classifier ◽  
Naïve Bayesian

We introduced a novel hybrid feature selection method based on rough conditional mutual information and Naive Bayesian classifier. Conditional mutual information is an important metric in feature selection, but it is hard to compute. We introduce a new measure called rough conditional mutual information which is based on rough sets; it is shown that the new measure can substitute Shannon’s conditional mutual information. Thus rough conditional mutual information can also be used to filter the irrelevant and redundant features. Subsequently, to reduce the feature and improve classification accuracy, a wrapper approach based on naive Bayesian classifier is used to search the optimal feature subset in the space of a candidate feature subset which is selected by filter model. Finally, the proposed algorithms are tested on several UCI datasets compared with other classical feature selection methods. The results show that our approach obtains not only high classification accuracy, but also the least number of selected features.

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