A Hybrid Dynamical Evolutionary Algorithm for Classification Rule Discovery
Yi Fan Jiang, Ling Wang, Li Chen · 2008
This paper studies hybrid dynamical evolutionary algorithm in the context of classification rule discovery. Nature inspired search algorithms such as genetic algorithms, Ant colonies and particle swarm optimization have been previously studied on data mining tasks, in particular, classification rule discovery. We extended this work by applying a hybrid algorithm which combines dynamical evolutionary algorithm and hill climbers and PSO, in same type of classification tasks. Our research focused on studying the hybrid algorithm of performance enhancements in classification rule discovery tasks. In this paper, we developed a hybrid algorithm based classifier and implemented different variations of it in Java. The algorithm has been benchmarked against the well-known decision tree induction algorithm C4.5. Results have been compared in terms of prediction accuracy,speed and comprehensibility. Our results showed that,the hybrid dynamical evolutionary algorithm based classifiers can compete with C4.5 in terms of prediction accuracy on certain data sets and outperform C4.5 in general in terms of comprehensibility. We also showed that hybrid algorithm could bring improvements in terms of execution speed in comparison to plain heuristic based classifiers.