Application of boruta feature selection in enhancing financial distress prediction performance of hybrid MLP_GA

Abstract

Financial distress prediction (FDP) has been a subject of extensive and ongoing research because of its significance in both internal and external components of enterprises including investors and creditors. Financial institutions must to be able to foresee financial difficulty in order to allow them for evaluating the financial health of businesses and individuals. Data pre-processing techniques have been found to increase the efficacy of prediction models, and many research consider feature selection as a pre-processing step before creating the models. The creation of efficient feature selection algorithms is one of the main challenges facing FDP. In this study, we present a hybrid methodology for predicting financial distress using a Multi-Layer Perceptron and Genetic Algorithm (MLP_GA) model with boruta automated feature selection. The proposed model is designed on genetic algorithm- based tuning of the crucial MLP hyperparameters, including Network depth, Dense layer activation function, Network width, and Network optimizer for a reliable prediction. This paper investigates how boruta algorithm based feature selection method improve the accuracy of our MLP_GA algorithm. We access the FDP performance utilizing samples of enterprises based in MENA area. Resampling with k-fold evaluation metrics is employed in the experiments. The experimental results indicate that the adoption of the boruta automated feature selection method has significantly enhanced the prediction performance and accuracy of the FDP model.