Learning acyclic rules based on Chaining Genetic Programming

Abstract

Multi-class problem is the class of problems having more than one classes in the data set. Bayesian Network (BN) is a well-known algorithm handling the multi-class problem and is applied to different areas. But BN cannot handle continuous values. In contrast, Genetic Programming (GP) can handle continuous values and produces classification rules. However, GP is possible to produce cyclic rules representing tautologic, in which are useless for inference and expert systems. Co-evolutionary Rule-chaining Genetic Programming (CRGP) is the first variant of GP handling the multi-class problem and produces acyclic classification rules [16]. It employs backward chaining inference to carry out classification based on the acquired acyclic rule set. It can handle multi-classes; it can avoid cyclic rules; it can handle input attributes with continuous values; and it can learn complex relationships among the attributes. In this paper, we propose a novel algorithm, the Chaining Genetic Programming (CGP) learning a set of acyclic rules and to produce better results than the CRGP's. The experimental results demonstrate that the proposed algorithm has the shorter learning process and can produce more accurate acyclic classification rules. © 2006 IEEE.