An evolutionary approach to rank class association rules with feedback mechanism

In this paper, we propose an evolutionary associative classification method by considering both adjustment of the order of the whole set of rules and refinement of the power of each single rule. We discover an interesting phenomenon that the classification performance could be improved if we import some prior-knowledge to re-rank the association rules, where the prior-knowledge could be some equations generated by combing the support and confidence values with various functions. We make use of Genetic Network Programming to automatically search the equation space for prior-knowledge. In addition to rank the rules by equations globally, we also develop a feedback mechanism to adjust the rules locally, by giving some rewards to good rules and penalties to bad ones. Because the proposed method is based on evolutionary computation, we could gradually refine the power of each rule so that it could affect the classification results more precisely. The experimental results on UCI benchmark datasets show that the proposed method could improve the classification accuracies effectively.

► Propose an evolutionary associative classification method by considering the local and global power of each rule.
► Improve the classification performance by some prior-knowledge to re-rank the association rules.
► Make use of Genetic Network Programming to automatically search the ranking equations for prior-knowledge.
► Develop a feedback mechanism to adjust the rules by giving rewards to good rules and penalties to bad ones.
► Improve the classification accuracies effectively on benchmark datasets.