A gradient approach for value weighted classification learning in naive Bayes

作者：

Highlights：

• Propose a new weighting method in the context of naive Bayes classification learning.

• Assign different weights for each feature value.

• A gradient approach for automatically calculating the weights of each feature value.

• Its performance is compared with that of other state-of-the-art methods.

• Experiments show the method could improve the performance of naive Bayes.

摘要

•Propose a new weighting method in the context of naive Bayes classification learning.•Assign different weights for each feature value.•A gradient approach for automatically calculating the weights of each feature value.•Its performance is compared with that of other state-of-the-art methods.•Experiments show the method could improve the performance of naive Bayes.

论文关键词：Classification,Bayesian learning,Feature weighting,Gradient descent

论文评审过程：Received 3 October 2014, Revised 6 April 2015, Accepted 18 April 2015, Available online 29 April 2015, Version of Record 16 July 2015.

论文官网地址：https://doi.org/10.1016/j.knosys.2015.04.020