FSOCP: feature selection via second-order cone programming

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.