An information-theoretic filter approach for value weighted classification learning in naive Bayes

作者：Lee Chang Hwan^*

来源：Data & Knowledge Engineering, 2018, 113: 116-128.

DOI：10.1016/j.datak.2017.11.002

• Feature weighting
• Feature selection
• Naive Bayes
• Kullback-Leibler

Assigning weights in features has been an important topic in some classification learning algorithms. In this paper, we propose a new paradigm of assigning weights in classification learning, called value weighting method. While the current weighting methods assign a weight to each feature, we assign a different weight to the values of each feature. The performance of naive Bayes learning with value weighting method is compared with that of some other traditional methods for a number of datasets. The experimental results show that the value weighting method could improve the performance of naive Bayes significantly.

• 出版日期2018-1