title: Improving classification accuracy and causal knowledge for better credit decisions.
abstract: Numerous studies have contributed to efforts to boost the accuracy of the credit scoring model. Especially interesting are recent studies which have successfully developed the hybrid approach, which advances classification accuracy by combining different machine learning techniques. However, to achieve better credit decisions, it is not enough merely to increase the accuracy of the credit scoring model. It is necessary to conduct meaningful supplementary analyses in order to obtain knowledge of causal relations, particularly in terms of significant conceptual patterns or structures involving attributes used in the credit scoring model. This paper proposes a solution of integrating data preprocessing strategies and the Bayesian network classifier with the tree augmented Na"ıve Bayes search algorithm, in order to improve classification accuracy and to obtain improved knowledge of causal patterns, thus enhancing the validity of credit decisions.
authors.affiliation: International Trade Department, Ta Hwa Institute of Technology, 1, Ta Hwa Road, Chiung-Lin, Hsin-Chu 307, Taiwan. itmike@thit.edu.tw
authors.forename: Wei-Wen
authors.initials: WW
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003657:Decision Making; D006801:Humans; D019359:Knowledge; D015233:Models, Statistical