Research Article

Marine Predators Algorithm for Compact Feature Selection on the Wisconsin Diagnostic Breast Cancer Benchmark

Authors

Javad Pourqasem * Department of Computer Engineering, Urmia University, Urmia, Iran.
Bhumika Diwan Manav Rachna University, Faridabad, Haryana, India.
Zeinab Parandavar Department of Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran.

https://doi.org/10.22105/scfa.v3i2.104

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Abstract

This study evaluates the Marine Predators Algorithm (MPA) as a binary wrapper feature selector for a support vector machine on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The scalar objective combines cross-validation classification error and normalized subset cardinality with accuracy weight 0.9. Ten comparator optimizers were evaluated under a reported common population size of 20 and 100 iterations over 30 runs. The recorded tables give MPA the highest mean accuracy, 98.49%, and the smallest mean subset, 11.13 of 30 features, a 62.9% reduction. These summaries describe a single retrospective benchmark. Their interpretation is conditional on resolving the reported accuracy, sensitivity and specificity aggregation and on recovering the paired run-level outputs and evaluation counts. The supplied one-sided Wilcoxon p-values are nominal comparisons rather than evidence of superiority after every possible multiplicity correction. The results motivate further evaluation of the wrapper; they do not establish early detection, biological validation or clinical readiness.

Keywords

Marine predators algorithm Feature selection Breast cancer diagnosis Metaheuristic optimization Wrapper method Support vector machine WDBC dataset

References (14)

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Published 2026-06-12
Issue Vol. 3 No. 2 (2026): Soft Computing Fusion with Applications (SCFA)
Section Articles
Document Type Research Article
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How to Cite

Pourqasem, J. ., Diwan, B. ., & Parandavar, Z. . (2026). Marine Predators Algorithm for Compact Feature Selection on the Wisconsin Diagnostic Breast Cancer Benchmark. Soft Computing Fusion With Applications , 3(2), 114-125. https://doi.org/10.22105/scfa.v3i2.104

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