Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms

This paper presents a proposal of a rule induction algorithm selecting a rule quality measure adaptively. The quality measure plays the role of an optimization criterion of the generated rules. Nine quality measures applied by the algorithm are presented and discussed in the paper. It is shown experimentally that the proposed algorithm provides us with obtaining a classifier of the best quality. During experiments, three criteria of the classifier quality were considered: overall accuracy, balanced accuracy (average accuracy of decision classes), and complexity of the classifier (understood to mean the number of induced rules). The experiments were carried out on 34 data sets coming from the UCI machine learning repository. Moreover, a proposal of four-rule filtration algorithms is presented in the paper. Their task is to limit the number of rules in the classifier. In particular, filtration influence on the classifier quality is studied.