Comparison of profit-based multi-objective approaches for feature selection in credit scoring

Naomi Simumba, Suguru Okami, Akira Kodaka, Naohiko Kohtake

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Feature selection is crucial to the credit-scoring process, allowing for the removal of irrel-evant 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 pop-ular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demon-strate 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.

Original languageEnglish
Article number260
Issue number9
Publication statusPublished - 2021 Sept


  • Credit evaluation
  • Feature selection
  • Multi-objective optimization
  • Profit scoring

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics
  • Computational Mathematics


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