An Enhanced Ant Colony Optimisation Algorithm with the Hellinger Distance for Shariah-Compliant Securities Companies Bankruptcy Prediction

• Published in: JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY-MALAYSIA
• Main Authors: Zainol, Annuur Zakiah; Saian, Rizauddin; Kin, Teoh Yeong; Razali, Muhamad Hasbullah Mohd; Abu Bakar, Sumarni
• Format: Article
• Language: English
• Published: UNIV UTARA MALAYSIA PRESS 2024
• Subjects: Computer Science
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001155532400005
• ISSN: 1675-414X; 2180-3862
• DOI: 10.32890/jict2024.23.1.1

This study addresses the challenge of applying ant colony optimisation algorithms to imbalanced datasets, focusing on a bankruptcy dataset. The application of ant colony optimization (ACO) algorithms has been limited by their performance on imbalanced datasets, particularly within bankruptcy prediction where the some of bankruptcy cases leads to skewed data distributions. Traditional ACO algorithms, including the original Ant -Miner, often fail to accurately classify minority classes, which is a critical shortcoming in the context of financial distress analysis. Hence, this study proposes an improved algorithm, the Hellinger Distance Ant -Miner (HD-AntMiner), which employs Hellinger distance as the heuristic for ants to gauge the similarity or dissimilarity between probability distributions. The effectiveness of HD-AntMiner is benchmarked against established classifiers-PART and J48-as well as the conventional Ant -Miner, using public datasets and a specialized dataset of 759 Shariahcompliant securities companies in Malaysia. Utilising the Friedman test and F -score for validation, HD-AntMiner demonstrates superior performance in handling imbalanced datasets compared to other algorithms, as affirmed by the Friedman test. The F -score analysis highlights HD-AntMiner's excellence, achieving the highest F -score for Breast -cancer and Credit -g datasets. When applied to the Shariahcompliant dataset, HD-AntMiner is compared with Ant -Miner and validated through a t -test and F -score. The t -test results confirm HD-AntMiner's higher accuracy than Ant -Miner, while the F -score indicates superior performance across multiple years in the Shariahcompliant dataset. Although the number of rules and conditions is not statistically significant, HD-AntMiner emerges as a robust algorithm for enhancing classification accuracy in imbalanced datasets, particularly in the context of Shariah-compliant securities prediction.