Al-Behadili, Hayder Naser Khraibet and Sagban, Rafid and Ku-Mahamud, Ku Ruhana (2020) Adaptive parameter control strategy for ant-miner classification algorithm. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 8 (1). pp. 149-162. ISSN 2089-3272

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Rule Induction, Data Mining, Parameter Control, Metaheuristic, Swarm Intelligent, Ant Colony Optimization
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	10 Nov 2020 05:36
Last Modified:	10 Nov 2020 05:36
URI:	https://repo.uum.edu.my/id/eprint/27854

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