Island Model Genetic Algorithm for Feature Selection in Non-Traditional Credit Risk Evaluation

Liu，Yue; Ghandar，Adam; Theodoropoulos，Georgios

10.1109/CEC.2019.8790057

2019-06-01

978-1-7281-2154-3

2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

2771-2778

10-13 June 2019

Wellington, New zealand

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

As digital infrastructure expands in new regions of the globe, developing ways to include more diverse information in financial decisions is important. However, making use of novel data sources requires developing methods to evaluate credit with diverse and complex datasets with missing information, dynamic patterns and relationships with decision recommendations, and larger feature sets. Feature selection is one approach that can support the application of machine learning to dynamically build models for credit evaluation with complex data. Genetic algorithms (GAs) have been proved to reach good performance in other research, with high computation cost though. In this paper, we review existing GA approaches and test and develop a novel method based on niching and the use of subpopulations with different data for fitness evaluation. This formulation allows less computation cost, even with better prediction performance in feature selection. In further experiments, we compare the proposed GA-based feature selection approaches in four traditional credit datasets and a novel emerging market dataset from China. The results indicate that the advanced GA-based feature selection methods perform more effectively.

credit scoring evolutionary computation feature selection finance applications machine learning

第一

英语

EI ; CPCI-S

Engineering ; Mathematical & Computational Biology

Engineering, Electrical & Electronic ; Mathematical & Computational Biology

WOS:000502087102101

20193507373749

Calculations ; Evolutionary algorithms ; Genetic algorithms ; Learning systems ; Machine learning ; Risk assessment

Accidents and Accident Prevention:914.1 ; Mathematics:921

2-s2.0-85071317263

Scopus

Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Liu，Yue,Ghandar，Adam,Theodoropoulos，Georgios. Island Model Genetic Algorithm for Feature Selection in Non-Traditional Credit Risk Evaluation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:2771-2778.