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A Systematic Analysis on the Reproducibility of Results in Bio-Inspired Optimization based Feature Selection Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 69 |
| Year of Publication: 2025 |
| Authors: A. Anitha |
10.5120/ijca2025926159
|
A. Anitha . A Systematic Analysis on the Reproducibility of Results in Bio-Inspired Optimization based Feature Selection Algorithm. International Journal of Computer Applications. 187, 69 ( Dec 2025), 43-46. DOI=10.5120/ijca2025926159
Abstract
Reproducibility is a foundation of the scientific method, signifying that when diverse researchers autonomously recreate an experiment by employing the same approaches, they should be reliable and consistently yield the same outcomes. In nature inspired optimization-based feature selection algorithms, irreproducibility may be caused by several factors including the non-convexity nature of the objective, initialization of random values, non-deterministic aspects of training like data shuffling, parallelism, random scheduling, variation in hardware, and round off quantization errors. In this study, the analysis of reproducibility of results in random search bat optimization algorithm for feature selection is conducted for Electrohysterogram (EHG) signals to assess the consistency of the algorithm. The results demonstrated that there was some variability in selected feature sets when the trial process is repeated from 1 to 20 with different bat size. Also, the method is very sensitive to initial parameters in random search process which may require further analysis to improve consistency and robustness. The study's outcomes may underscore the importance of reproducibility in feature selection research, emphasizing that it is crucial for ensuring the robustness and credibility of findings.
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