Adaptive parameter control strategy for ant-miner classification algorithm
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 inc...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering and Science
2020
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| Subjects: | |
| 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 |
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| Summary: | 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. |
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