Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem

Abstract

Financial distress prediction (FDP) is a significant issue investigated by researchers, credit institutions and banks. Although extensive research has been conducted in this area, applications of combined feature selection (FS) methods and classification models are subjects that have been addressed intensely in recent years. One of the most important issues in the FDP problem is to employ an effective FS algorithm, leading to an acceptable level of performance accuracy in the implementation stage. Hence, this study primarily attempted to introduce a precise FS model and compared the obtained results with those of other conventional models tackling FDP in terms of accuracy. The proposed method involved the sequential floating forward selection (SFFS) algorithm applied as a wrapper FS technique to determine the best subset of features. At the classification stage, the support vector machine (SVM), owing to its good performance, demonstrated in numerous studies, in solving classification problems, was deployed. The performance of the proposed method was compared with those of other current well-known FS methods including artificial bee colony (ABC), genetic algorithm (GA) and sequential forward selection (SFS) (all of which are categorized under wrapper methods), and principal component analysis (PCA), relief and information gain (IG) (best known as filter techniques) for our given datasets. The results indicated that a combined model of SVM based on the SFFS approach can yield greater accuracy than the other methods applied for our defined domestic and foreign datasets. Therefore, the SFFS-SVM ensemble classifier can be considered a promising addition to existent models when confronting the FDP issue.