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An empirical study of the naive Bayes classifier

An empirical study of the naive Bayes classifier,I. Rish

An empirical study of the naive Bayes classifier   (Citations: 149)
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The naive Bayes classifier greatly simplify learn- ing by assuming that features are independent given class. Although independence is generally a poor assumption, in practice naive Bayes often competes well with more sophisticated classifiers. Our broad goal is to understand the data character- istics which affect the performance of naive Bayes. Our approach uses Monte Carlo simulations that al- low a systematic study of classification accuracy for several classes of randomly generated prob- lems. We analyze the impact of the distribution entropy on the classification error, showing that low-entropy feature distributions yield good per- formance of naive Bayes. We also demonstrate that naive Bayes works well for certain nearly- functional feature dependencies, thus reaching its best performance in two opposite cases: completely independent features (as expected) and function- ally dependent features (which is surprising). An- other surprising result is that the accuracy of naive Bayes is not directly correlated with the degree of feature dependencies measured as the class- conditional mutual information between the fea- tures. Instead, a better predictor of naive Bayes ac- curacy is the amount of information about the class that is lost because of the independence assump- tion.
Published in 2001.
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