Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

DongHong Sun, Li Liu, Peng Zhang, Xingquan Zhu and Yong Shi
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DongHong Sun: Tsinghua University, China
Li Liu: University of Technology, Sydney, Australia
Peng Zhang: Chinese Academy of Sciences, China
Xingquan Zhu: University of Technology, Sydney, Australia
Yong Shi: Chinese Academy of Sciences, China, and University of Nebraska at Omaha, USA

International Journal of Data Warehousing and Mining (IJDWM), 2011, vol. 7, issue 3, 88-101

Abstract: Due to the flexibility of multi-criteria optimization, Regularized Multiple Criteria Linear Programming (RMCLP) has received attention in decision support systems. Numerous theoretical and empirical studies have demonstrated that RMCLP is effective and efficient in classifying large scale data sets. However, a possible limitation of RMCLP is poor interpretability and low comprehensibility for end users and experts. This deficiency has limited RMCLP’s use in many real-world applications where both accuracy and transparency of decision making are required, such as in Customer Relationship Management (CRM) and Credit Card Portfolio Management. In this paper, the authors present a clustering based rule extraction method to extract explainable and understandable rules from the RMCLP model. Experiments on both synthetic and real world data sets demonstrate that this rule extraction method can effectively extract explicit decision rules from RMCLP with only a small compromise in performance.

Date: 2011
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