FSOCP: feature selection via second-order cone programming

Buse Çisil Güldoğuş () and Süreyya Özögür-Akyüz ()
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Buse Çisil Güldoğuş: Bahcesehir University
Süreyya Özögür-Akyüz: Bahcesehir University

Central European Journal of Operations Research, 2025, vol. 33, issue 1, No 3, 64 pages

Abstract: Abstract Feature selection is an important factor of accurately classifying high dimensional data sets by identifying relevant features and improving classification accuracy. The use of feature selection in operations research allows for the identification of relevant features and the creation of optimal subsets of features for improved predictive performance. This paper proposes a novel feature selection algorithm inspired from ensemble pruning which involves the use of second-order conic programming modeled as an embedded feature selection technique with neural networks, named feature selection via second order cone programming (FSOCP). The proposed FSOCP algorithm trains features individually on a neural network and generates a probability class distribution and prediction, allowing the second-order conic programming model to determine the most important features for improved classification accuracies. The algorithm is evaluated on multiple synthetic data sets and compared with other feature selection techniques, demonstrating its promising potential as a feature selection approach.

Keywords: Feature selection; Feature extraction; Neural networks; Ensemble pruning; Second order cone programming (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10100-023-00903-y

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