Affiliations: [a] College of Computer Science and Technology, Jilin University, Changchun, Jilin, China | [b] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China | [c] School of Economics, Nanjing Audit University, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Minghui Sun, College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China. E-mail: smh@jlu.edu.cn.
Abstract: Bayesian network classifiers (BNCs) have proved their effectiveness and efficiency in the supervised learning framework. Numerous variations of conditional independence assumption have been proposed to address the issue of NP-hard structure learning of BNC. However, researchers focus on identifying conditional dependence rather than conditional independence, and information-theoretic criteria cannot identify the diversity in conditional (in)dependencies for different instances. In this paper, the maximum correlation criterion and minimum dependence criterion are introduced to sort attributes and identify conditional independencies, respectively. The heuristic search strategy is applied to find possible global solution for achieving the trade-off between significant dependency relationships and independence assumption. Our extensive experimental evaluation on widely used benchmark data sets reveals that the proposed algorithm achieves competitive classification performance compared to state-of-the-art single model learners (e.g., TAN, KDB, KNN and SVM) and ensemble learners (e.g., ATAN and AODE).
Keywords: Bayesian network classifier, maximum correlation criterion, minimum dependence criterion, conditional independence, conditional mutual information
