DOI : https://doi.org/10.18517/ijaseit.11.1.10826

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Hayder Naser Khraibet Al-Behadili (1), Ku Ruhana Ku-Mahamud (2), Rafid Sagban (3)
(1) Computer Science Department, Shatt Alarab University College, Basra,61001, Iraq
(2) School of Computing, Universiti Utara Malaysia, Kedah, 06010 Sintok, Kedah, Malaysia
(3) Department of Software, University of Babylon, Babylon,51002, Iraq
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How to cite (IJASEIT) :
Al-Behadili, Hayder Naser Khraibet, et al. “Genetic-Based Pruning Technique for Ant-Miner Classification Algorithm”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, Feb. 2021, pp. 304-11, doi:10.18517/ijaseit.11.1.10826.
Citation Format :
Ant colony optimization (ACO) is a well-known algorithm from swarm intelligence that plays an essential role in obtaining rich solutions to complex problems with wide search space. ACO is successfully applied to different application problems involving rules-based classification through an ant-miner classifier. However, in the ant-miner classifier, rule-pruning suffers from the problem of nesting effect origins from the method of greedy Sequential Backward Selection (SBS) in term selection, thereby depriving the opportunity of obtaining a good pruned rule by adding/removing the terms during the pruning process. This paper presents an extension to the Ant-Miner, namely the genetic algorithm Ant-Miner (GA-Ant Miner), which incorporates the use of GA as a key aspect in the design and implementation of a new rule pruning technique. This pruning technique consists of three fundamental procedures: an initial population Ant-Miner, crossover to prune the rule, and mutation to diversify the pruned classification rule. The GA-Ant Miner performance is tested and compared with the most related ant-mining classifiers, including the original Ant-Miner, ACO/ PSO2, TACO-Miner, CAnt-Miner, and Ant-Miner with a hybrid pruner, across various public available UCI datasets. These datasets are varied in terms of instance number, feature size, class number, and the application domains. Overall, the performance results indicate that the GA-Ant Miner classifier outperforms the other five classifiers in the classification accuracy and model size. Furthermore, the experimental results using statistical test prove that GA-Ant Miner is the best classifier when considering the multi objectives (i.e., accuracy and model size ranks).

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