A case-based reasoning model that uses preference theory functions for credit scoring |
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| Authors: | Sanja Vukovic Boris Delibasic Ana Uzelac Milija Suknovic |
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| Affiliation: | 1. Department of Technology Management for Innovation, School of Engineering, The University of Tokyo, Tokyo, Japan;2. Center for Service Engineering, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China;2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China;3. College of Engineering and Technology, Southwest University, Chongqing, 400715, China;1. Department of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran;2. Department of Industrial Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran;1. NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal;2. NOVA Information Management School (NOVA IMS) & Université Paris-Dauphine, Portugal |
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| Abstract: | We propose a case-based reasoning (CBR) model that uses preference theory functions for similarity measurements between cases. As it is hard to select the right preference function for every feature and set the appropriate parameters, a genetic algorithm is used for choosing the right preference functions, or more precisely, for setting the parameters of each preference function, as to set attribute weights. The proposed model is compared to the well-known k-nearest neighbour (k-NN) model based on the Euclidean distance measure. It has been evaluated on three different benchmark datasets, while its accuracy has been measured with 10-fold cross-validation test. The experimental results show that the proposed approach can, in some cases, outperform the traditional k-NN classifier. |
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