GEPCLASS: A Classification Rule Discovery Tool Using Gene Expression Programming

Abstract

AbstractThis work describes the use of a recently proposed technique – gene expression programming – for knowledge discovery in the data mining task of data classification. We propose a new method for rule encoding and genetic operators that preserve rule integrity, and implemented a system, named GEPCLASS. Due to its encoding scheme, the system allows the automatic discovery of flexible rules, better fitted to data. The performance of GEPCLASS was compared with two genetic programming systems and with C4.5, over four data sets in a five-fold cross-validation procedure. The predictive accuracy for the methods compared were similar, but the computational effort needed by GEPCLASS was significantly smaller than the other. GEPCLASS was able to find simple and accurate rules as it can handle continuous and categorical attributes.KeywordsEncode SchemeGenetic OperatorGene Expression ProgrammingClosure PropertyData Mining TaskThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.