Selective AnDE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI)

Abstract

ABSTRACT Attribute selection has been proved to be an effective trick to strengthen the classification capability of Bayesian network classifiers, such as Averaged -Dependence Estimators (AnDE). However, conventional mutual information-based attribute ranking considers only the correlation between the attribute and the class, regardless of the redundancies among the attributes. In this paper, we propose a new ranking approach, called Maximin Conditional Mutual Information (MMCMI), which first minimises the conditional mutual information for any unsorted attribute with regard to the sorted attribute sequence, and then maximise the minimal conditional mutual information within all unsorted attributes. When ranking the very first attribute, the mutual information with the class is maximised within all attributes. Extensive empirical results demonstrate that the MMCMI ranking approach together with attribute selection framework achieves significantly superior classification performance and less classification time with respect to regular AnDE and the mutual information counterparts.