Class-specific attribute weighted naive Bayes. (April 2019)

• Record Type: Journal Article
• Authors: Jiang, Liangxiao; Zhang, Lungan; Yu, Liangjun; Wang, Dianhong
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 321
• Page End: 330
• Publication Date: 2019-04
• Subjects: Naive Bayes -- Attribute weighting -- Weight optimization; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.11.032
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Almost all existing attribute weighting approaches to naive Bayes are class-independent. We propose a new class-specific attribute weighting paradigm for naive Bayes. The resulting model is called class-specific attribute weighted naive Bayes (CAWNB). To learn CAWNB, we propose two gradient-based learning algorithms. The experimental results validate the effectiveness of the proposed algorithms. Abstract: Due to its easiness to construct and interpret, along with its good performance, naive Bayes (NB) is widely used to address classification problems in real-world applications. In order to alleviate its conditional independence assumption, a mass of attribute weighting approaches have been proposed. However, almost all these approaches assign each attribute a same (global) weight for all classes. In this paper, we call them the general attribute weighting and argue that for NB attribute weighting should be class-specific (class-dependent). Based on this premise, we propose a new paradigm for attribute weighting called the class-specific attribute weighting, which discriminatively assigns each attribute a specific weight for each class. We call the resulting model class-specific attribute weighted naive Bayes (CAWNB). CAWNB selects class-specific attribute weights to maximize the conditional log likelihood (CLL) objective function or minimize the mean squared error (MSE) objective function, and thus two different versions are created, which we denote as CAWNB CLL and CAWNB MSE, respectively. Extensive empirical studies show that CAWNB CLL and CAWNB MSE all obtain more satisfactory experimental results compared with NB and other existing state-of-the-art general attribute weighting approaches. We believe that for NB class-specific attribute weighting could be a more fine-grained attribute weighting approach than general attribute weighting.