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Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

DongHong Sun, Li Liu, Peng Zhang, Xingquan Zhu, Yong Shi
Copyright: © 2011 |Volume: 7 |Issue: 3 |Pages: 14
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781613506356|DOI: 10.4018/jdwm.2011070104
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MLA

Sun, DongHong, et al. "Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model." IJDWM vol.7, no.3 2011: pp.88-101. http://doi.org/10.4018/jdwm.2011070104

APA

Sun, D., Liu, L., Zhang, P., Zhu, X., & Shi, Y. (2011). Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model. International Journal of Data Warehousing and Mining (IJDWM), 7(3), 88-101. http://doi.org/10.4018/jdwm.2011070104

Chicago

Sun, DongHong, et al. "Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model," International Journal of Data Warehousing and Mining (IJDWM) 7, no.3: 88-101. http://doi.org/10.4018/jdwm.2011070104

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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.

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