An attribute value frequency-based instance weighting filter for naive Bayes. Issue 2 (4th March 2019)

• Record Type: Journal Article
• Authors: Xu, Wenqiang; Jiang, Liangxiao; Yu, Liangjun
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 31:Issue 2(2019)
• Page Start: 225
• Page End: 236
• Publication Date: 2019-03-04
• Subjects: Naive Bayes -- instance weighting -- attribute value frequency -- classification; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2018.1544284
• Languages: English
• ISSNs: 0952-813X

Abstract

ABSTRACT: Due to its simplicity, efficiency and efficacy, naive Bayes (NB) continues to be one of the top 10 data mining algorithms. A mass of improved approaches to NB have been proposed to weaken its conditional independence assumption. However, there has been little work, up to the present, on instance weighting filter approaches to NB. In this paper, we propose a simple, efficient, and effective instance weighting filter approach to NB. We call it attribute (feature) value frequency-based instance weighting and denote the resulting improved model as attribute value frequency weighted naive Bayes (AVFWNB). In AVFWNB, the weight of each training instance is defined as the inner product of its attribute value frequency vector and the attribute value number vector. The experimental results on 36 widely used classification problems show that AVFWNB significantly outperforms NB, yet at the same time maintains the computational simplicity that characterizes NB.