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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Muhamad Hasbullah Bin Mohd Razali
Universiti Teknologi MARA

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A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets Muhamad Hasbullah Bin Mohd Razali; Rizauddin Bin Saian; Yap Bee Wah; Ku Ruhana Ku-Mahamud
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp412-419

Abstract

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 hellinger-ant-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-insensitiveness of hellinger distance. The statistical significance test shows that HATM has higher mean BACC score than ATM.
Co-Authors Ku Ruhana Ku-Mahamud Rizauddin Bin Saian Yap Bee Wah