Error Correction Method in Classification by Using Multiple-Criteria and Multiple-Constraint Levels Linear Programming


  • Bo Wang School of Mathematical Sciences, Graduate University of the Chinese Academy of Sciences, Beijing 100190, China
  • Yong Shi 1. Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China and 2. College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA


Classification, Two Types of Error, Multiple-Criteria Linear Programming, Multiple-Criteria and Multiple-Constraint Levels Linear Programming, Decision Supporting System


In classification based on multiple-criteria linear programming (MCLP), we need to find the optimal solution of the MCLP problem as a classifier. According to dual theory, multiple criteria can be switched to multiple constraint levels, and vice versa. A MCLP problem can be logically extended into a multiple-criteria and multiple-constraint levels linear programming (MC2LP) problem. In many applications, such as credit card account classification, how to handle two types of error is a key issue. The errors can be caused by a fixed cutoff between a "Good" group and a "Bad" group. Two types of error can be systematically corrected by using the structure of MC2LP, which allows two alterable cutoffs. In order to do so, a penalty (or cost) is imposed to find the potential solution for all possible trade-offs in solving MC2LP problem. Some correction strategies can be investigated by the solution procedure. Furthermore, a framework of decision supporting system can be illustrated for various real-life applications of the proposed method.

Author Biography

Bo Wang, School of Mathematical Sciences, Graduate University of the Chinese Academy of Sciences, Beijing 100190, China

Department of Mathematics and Computer Science


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