Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm

Abstract

The proposition of adaptive selection of rule quality measures during rules induction is presented in the paper. In the applied algorithm the measures decide about a form of elementary conditions in a rule premise and monitor a pruning process. An influence of filtration algorithms on classification accuracy and a number of obtained rules is also presented. The analysis has been done on twenty one benchmark data sets. © 2011 Springer-Verlag.