Adaptive parameter control strategy for ant-miner classification algorithm

• Main Authors: Al-Behadili, Hayder Naser Khraibet, Sagban, Rafid, Ku-Mahamud, Ku Ruhana
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/27854/1/IJEEI%208%201%202020%20149%20162.pdf; http://repo.uum.edu.my/27854/; http://section.iaesonline.com/index.php/IJEEI/article/view/1423

Pruning is the popular framework for preventing the dilemma of over fitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-Ant Miner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers.