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El Aboudi, Naoual
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Computer Science & IT Electrical & Electronics Engineering

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Automated feature selection using improved migrating birds optimization for enhanced medical diagnosis El Aboudi, Naoual; Riouali, Youness; Maminou, Ahmed Reda; Jabri, Hassane; Benhlima, Laila
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3159-3167

Abstract

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental machine learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms.
Co-Authors Benhlima, Laila Jabri, Hassane Maminou, Ahmed Reda Riouali, Youness