Assigning Different Weights to Feature Values in Naive Bayes
Assigning weights in features has been an important topic in some classification learning algorithms. While the current weighting methods assign a weight to each feature, in this paper, we assign a different weight to the values of each feature. The performance of naive Bayes learning with value-based weighting method is compared with that of some other traditional methods for a number of datasets.
KeywordsFeature weighting Feature selection Naive Bayes Kullback-Leibler
This work was supported by the Korea Research Foundation (KRF) grant funded by the Korea government (MEST) (No. 2014-R1A2A1A11051011).
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