Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring

Abstract

Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…Due to the high number of binary feature selection-related work in the literature, the objective function formulated includes either classification accuracy maximization or minimization of the number of subsets selected. Moreover, to combine the conflicting objectives, most works in the literature constructed multi-objective functions to solve feature selection issues [ 9 , 62 , 65 , 146 , 223 , 277 ] and converted the multi-objective problem into a single objective problem through the application of weights on both objectives then performed the algorithm learning. This method has effectively and efficiently optimized the fitness function and located the optimal feature subset within particular datasets.…”

“Algorithms in Multi-Objective Optimization”: Foreword by the Guest Editor

“…The third paper of the Special Issue titled "Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring" by Simumba et al [8] concentrates its focus exactly on the latter topic and in particular on feature selection, which is crucial to credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Indeed, conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit.…”