A Multi-criteria Convex Quadratic Programming model for credit data analysis

Authors:
Yi Peng;Gang Kou;Yong Shi;Zhengxin Chen
Affiliations:
School of Management and Economy, University of Electronic Science and Technology of China, Chengdu, 610054, PR China and College of Information Science & Technology, University of Nebraska at Oma ...;The Thomson Corporation, R&D, 610 Opperman Drive, Eagan, MN 55123, USA and College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA;College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA and CAS Research Center on Fictitious Economy and Data Sciences, Beijing 100080, PR China;College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Venue:
Decision Support Systems
Year:
2008

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Abstract

Speed and scalability are two essential issues in data mining and knowledge discovery. This paper proposed a mathematical programming model that addresses these two issues and applied the model to Credit Classification Problems. The proposed Multi-criteria Convex Quadric Programming (MCQP) model is highly efficient (computing time complexity O(n^1^.^5^-^2)) and scalable to massive problems (size of O(10^9)) because it only needs to solve linear equations to find the global optimal solution. Kernel functions were introduced to the model to solve nonlinear problems. In addition, the theoretical relationship between the proposed MCQP model and SVM was discussed.