Rule-evolver: An evolutionary approach for data mining

Abstract

This paper presents a genetic model and a software tool (Rule-Evolver) for the classification of records in Databases (DB). The model is based on the evolution of association rules of the IF-THEN type, which provide a high level of accuracy and coverage. The modeling of the Genetic Algorithm consists of the definition of chromosomes representation, the evaluation function, and the genetic operators. The Rule-Evolver is a tool that provides an environment for the evaluation of the genetic model and implements the interface with DBs. The case studies evaluate the performance of the model in several benchmark DBs. The results obtained are compared with those of other models, such as Artificial Neural Nets, Neuro-Fuzzy Systems and Statistical Models.