OptSelect: An algorithm for ensemble feature selection and stability assessment

Recent studies have shown that ensemble feature selection approaches can improve the robustness and stability of final classification models. Existing methods for aggregating feature lists from different methods require use of arbitrary thresholds for selecting the top ranked features and are often based on metrics independent of the classification accuracy while selecting the optimal set. In this paper, we develop the OptSelect tool for ensemble feature selection and stability assessment of individual features for improved biomarker discovery. The software tool is packaged in R for broad dis-semination. OptSelect is a multi agent-based stochastic optimization tool designed for ensemble feature selection. Stage one involves function perturbation, where ranked list of features is generated using multiple feature selection methods. Stage two in-volves data perturbation, where feature selection is performed within randomly selected learning sets of the training da-ta. The agents are assigned to different behavior states and move according to a binary PSO algorithm. A multi-objective fitness function is used to evaluate the classification accuracy of the agents. We evaluate OptSelect system performance using the random probe method testing on five publicly available microarray datasets. The performance is compared with single feature selection techniques and existing aggregation methods. The results show that OptSelect improves classification accuracy when compared to both individual and existing rank aggregation methods. The PSO algorithm is able to uncover important discriminatory features for predicting COVID-19 disease severity, demonstrating its important role within the optSelect tool. The algorithm is incorporated into an R package and disseminated via GitHub: https://github.com/kuppa12/optSe1ect.