期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：4
页码：657-671
出版社：Journal of Theoretical and Applied
摘要：This research presents the ILS-AntMiner rules-based algorithm, a hybrid Iterated Local Search and Ant Colony Optimization, to improve classification accuracy and the size of the classification model. This hybridisation aims to enhance the classification performance in both accuracy and simplicity by increasing the profit of neighbourhood structures in the exploitation mechanism. The experimental results in this research are compared with the most related ant-mining classifiers, including ACO/PSO2 and ACO/SA across various datasets. The results indicate that the proposed classification algorithm can effectively search the training space based on multiple structures to escape from local optima and achieve high classification accuracy and model size.
