DISCRETIZATION OF CONTINUOUS ATTRIBUTES USING GENETIC AND ENTROPY BASED CONCEPT LEARNER

Abstract: Genetic Algorithm (GA) based concept learner is widely used in supervised learning sys-tem for attribute based spaces. The conventional GA based operators can not directly deal with continuous attributes. We have used two separate approaches one is pure Genetic Algorithm and another is Entropy based approach coupled with Genetic Algorithm for converting continuous at-tributes into discrete ones. Later on these con-verted discrete attributes have been used in tradi-tional GA based concept learner to enhance its performance. In our experiments with benchmark data set it has been revealed that statistically sig-nificant improvement has been achieved with the proposed technique