Multi-objective Particle Swarm Optimization for Feature Selection in Credit Scoring

• Published in: Mining Data for Financial Applications Vol. 12591; pp. 68 - 76
• Main Authors: Kozodoi, Nikita, Lessmann, Stefan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Credit scoring; Feature selection; Multi-objective optimization
• ISBN: 9783030669805; 3030669807
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-66981-2_6

Credit scoring refers to the use of statistical models to support loan approval decisions. An ever-increasing availability of data on potential borrowers emphasizes the importance of feature selection for scoring models. Traditionally, feature selection has been viewed as a single-objective task. Recent research demonstrates the effectiveness of multi-objective approaches. We propose a novel multi-objective feature selection framework for credit scoring that extends previous work by taking into account data acquisition costs and employing a state-of-the-art particle swarm optimization algorithm. Our framework optimizes three fitness functions: the number of features, data acquisition costs and the AUC. Experiments on nine credit scoring data sets demonstrate a highly competitive performance of the proposed framework.