Argument based generalization of MODLEM rule induction algorithm

Abstract

Argument based learning allows experts to express their domain, local knowledge about the circumstances of making classification decisions for some learning examples. In this paper we have incorporated this idea in rule induction as a generalization of the MODLEM algorithm. To adjust the algorithm to the redefined task, a new measure for evaluating rule conditions and a new classification strategy with rules had to be introduced. Experimental studies showed that using arguments improved classification accuracy and structure of rules. Moreover the proper argumentation improved recognition of the minority class in imbalanced data without essential decreasing recognition of the majority classes. © 2010 Springer-Verlag Berlin Heidelberg.