A constraint-based genetic algorithm approach for mining classification rules

Data mining is an information extraction process that aims to discover valuable knowledge in databases. Existing genetic algorithms (GAs) designed for rule induction evaluates the rules as a whole via a fitness function. Major drawbacks of GAs for rule induction include computation inefficiency, accuracy and rule expressiveness. In this paper, we propose a constraint-based genetic algorithm (CBGA) approach to reveal more accurate and significant classification rules. This approach allows constraints to be specified as relationships among attributes according to predefined requirements, user's preferences, or partial knowledge in the form of a constraint network. The constraint-based reasoning is employed to produce valid chromosomes using constraint propagation to ensure the genes to comply with the predefined constraint network. The proposed approach is compared with a regular GA and C4.5 using two UCI repository data sets. Better classification accurate rates from CBGA are demonstrated.