A gradient approach for value weighted classification learning in naive Bayes

•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.

Feature weighting has been an important topic in 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 proposed method is implemented in the context of naive Bayesian learning, and optimal weights of feature values are calculated using a gradient approach. The performance of naive Bayes learning with value weighting method is compared with that of other state-of-the-art methods for a number of datasets. The experimental results show that the value weighting method could improve the performance of naive Bayes significantly.