Sensitivity Analysis of Parameter Control in Leukemia Classification Using Variable-Length Particle Swarm Optimization - Ramadhani, Siti; Handayani, Lestari Handayani; Muhammad Fikri; Theam Foo Ng; Sumayyah Dzulkifly; Roziana Ariffin; Shir Li Wang (Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, 2025)
Machine learning has the potential to support hematologists in classifying leukemia by identifying abnormal chromosomes and specific gene markers. One effective technique for feature selection is Variable-Length Particle Swarm Optimization (VLPSO), where its performance depends heavily on parameter control, specifically the inertia weight (w) and acceleration factors (c), which regulate the search process. In previous VLPSO, static types of parameter control were applied to the  Factor, and time-varying types were used by the   Factor. Although its results showed good performance in VLPSO, there was no separation in the treatment of training data and test data, leaving a gap in understanding their impacts for real-world applications.  This study explores how different parameter control strategies (static, time-varying, and adaptive) affect the performance of VLPSO with two comparison adaptive parameter control approaches, Adaptive 1 and Adaptive 2, in the VLPSO framework, each designed to dynamically adjust the control parameters w and c in different ways. The 10-fold cross-validation shows that VLPSO with an Adaptive one-parameter setting achieves better generalization with low train-test differences, especially in Decision Tree and Naïve Bayes classifiers, though with higher variability. Adaptive 2-parameter setting of VLPSO offers more consistent results with narrower variability across different settings. Static methods are the least reliable, while time-varying controls show moderate but unstable performance. Adaptive parameter tuning is recommended to improve VLPSO's stability, flexibility, and classification accuracy in biomedical applications. The results provide recommendations for parameter settings using an adaptive approach that has been proven to enhance the performance of VLPSO