Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification

A large number of features lead to very high-dimensional data. The feature selection method reduces the dimension of data, increases the performance of prediction, and reduces the computation time. Feature selection is the process of selecting the optimal set of input features from a given data set in order to reduce the noise in data and keep the relevant features. The optimal feature subset contains all useful and relevant features and excludes any irrelevant feature that allows machine learning models to understand better and differentiate efficiently the patterns in data sets. In this article, we propose a binary hybrid metaheuristic-based algorithm for selecting the optimal feature subset. Concretely, the brain storm optimization algorithm is hybridized by the firefly algorithm and adopted as a wrapper method for feature selection problems on classification data sets. The proposed algorithm is evaluated on 21 data sets and compared with 11 metaheuristic algorithms. In addition, the proposed method is adopted for the coronavirus disease data set. The obtained experimental results substantiate the robustness of the proposed hybrid algorithm. It efficiently reduces and selects the feature subset and at the same time results in higher classification accuracy than other methods in the literature.

Automated summary

Feature selection methods can reduce the dimension of high-dimensional data, improve prediction performance, and reduce computation time. In this article, a binary hybrid metaheuristic-based algorithm is proposed for feature selection, which is evaluated on multiple datasets and outperforms other methods in terms of classification accuracy.