Hybrid Ant Colony Optimization and Genetic Algorithm for Rule Induction

In this study, a hybrid rule-based classifier namely, ant colony optimization/genetic algorithm ACO/GA is introduced to improve the classification accuracy of Ant-Miner classifier by using GA. The Ant-Miner classifier is efficient, useful and commonly used for solving rule-based classification problems in data mining. Ant-Miner, which is an ACO variant, suffers from local optimization problem which affects its performance. In our proposed hybrid ACO/GA algorithm, the ACO is responsible for generating classification rules and the GA improves the classification rules iteratively using the principles of multi-neighborhood structure (i.e., mutation and crossover) procedures to overcome the local optima problem. The performance of the proposed classifier was tested against other existing hybrid ant-mining classification algorithms namely, ACO/SA and ACO/PSO2 using classification accuracy, the number of discovered rules and model complexity. For the experiment, the 10-fold cross-validation procedure was used on 12 benchmark datasets from the University California Irwine machine learning repository. Experimental results show that the proposed hybridization was able to produce impressive results in all evaluation criteria.