A gradient approach for value weighted classification learning in naive Bayes

作者：Lee Chang Hwan^*

来源：Knowledge-Based Systems, 2015, 85: 71-79.

DOI：10.1016/j.knosys.2015.04.020

• Classification
• Bayesian learning
• Feature weighting
• Gradient descent

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.

• 出版日期2015-9