Feature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction

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dc.contributor.authorTeerasarn, Chalanwich
dc.contributor.authorKimpan, Warangkhana
dc.date.accessioned2026-08-06T10:49:51Z
dc.date.available2026-08-06T10:49:51Z
dc.date.issued2025-01-01
dc.description.abstractThis research focuses on developing an efficient feature selection process using a hybrid technique combining Cuckoo Search algorithm and Firefly Algorithm. The Wisconsin Diagnostic Breast Cancer dataset is utilized to evaluate the capability of selecting significant features and eliminating irrelevant ones. The experimental results demonstrate that using the hybrid technique significantly improves the accuracy of machine learning compared to using the Cuckoo Search and Firefly Algorithm individually. Additionally, an analysis was conducted on the impact of splitting the dataset for training and testing, with splits of 70/30, 80/20, and 90/10. The experiments revealed a relationship between the number of selected features and the model accuracy. The findings from this study can serve as a guideline for developing appropriate feature selection process for complex data analysis problems.
dc.identifier.citationCacml 2025 2025 4th Asia Conference on Algorithms Computing and Machine Learning, 2025
dc.identifier.doi10.1109/CACML64929.2025.11010928
dc.identifier.other2-s2.0-105007761704
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16630
dc.sourceCacml 2025 2025 4th Asia Conference on Algorithms Computing and Machine Learning
dc.subjectBreast Cancer
dc.subjectCuckoo Search
dc.subjectFeature Selection
dc.subjectFirefly Algorithm
dc.subjectSwarm Intelligence
dc.titleFeature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction
dc.typeConference Paper

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