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A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

Mohd Razali, Muhamad Hasbullah and Saian, Rizauddin and Yap, Bee Wah and Ku-Mahamud, Ku Ruhana (2021) A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets. Indonesian Journal of Electrical Engineering and Computer Science, 21 (1). pp. 412-419. ISSN 2502-4752

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Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. However, real world applications commonly involved imbalanced class problem where the classes have different importance. This condition impeded the entropy-based heuristic of existing ATM algorithm to develop effective decision boundaries due to its biasness towards the dominant class. Consequently, the induced decision trees are dominated by the majority class which lack in predictive ability on the rare class. This study proposed an enhanced algorithm called hellingerant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. The proposed algorithm was compared to the existing algorithm, ATM in nine (9) publicly available imbalanced data sets. Simulation study reveals the superiority of HATM when the sample size increases with skewed class (Imbalanced Ratio < 50%). Experimental results demonstrate the performance of the existing algorithm measured by BACC has been improved due to the class skew in sensitiveness of hellinger distance. The statistical significance test shows that HATM has higher mean BACC scorethan ATM.

Item Type: Article
Uncontrolled Keywords: Ant colony optimization, Classification, Decision tree Hellinger distance Imbalanced learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Mrs. Norazmilah Yaakub
Date Deposited: 09 Nov 2020 00:22
Last Modified: 09 Nov 2020 00:22

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