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Dominance-based decision rule induction for multicriteria ranking

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Abstract

We consider the issue on decision rules induction for multicriteria ranking. Multiple Criteria Decision Analysis (MCDA) aims at giving people the knowledge of recommendation concerning a finite set of objects evaluated with multiple preference-ordered attributes (known as criteria). Dominance-based Rough Set Approach (DRSA) is a powerful tool for MCDA via assigning objects to several predefined and preference-ordered decision classes. Most of previous strategies are to induce a minimal set of “if…then…” rules. In this paper, we provide strategies to induce a new rule set as the substitution for the classical minimal rule set. The main contributions include: (1) providing methods to induce certain rules in two situations respectively: multi-criteria and mix-attributes; (2) providing the concept of believe factor and its three measuring degrees for exploring valuable uncertain information within rough boundary regions; (3) providing the properties of believe factor with explanations from the viewpoint of class-based rough model; (4) proposing an extended Net Flow Score method in consideration of both partial and total orders in multicriteria ranking, via our proposed decision rules. A numerical example is used for illustration of overall problem-solving procedures and for a comparison with the existing representative proposals.

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Acknowledgments

The authors are very grateful to the Editor-in-Chief, Professor Xizhao Wang, and the five anonymous reviewers for their careful, insightful, and constructive comments that lead to the improved version of this paper.

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Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR

    Junyi Chai & James N. K. Liu

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  1. Junyi Chai
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  2. James N. K. Liu
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Correspondence to Junyi Chai.

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Chai, J., Liu, J.N.K. Dominance-based decision rule induction for multicriteria ranking. Int. J. Mach. Learn. & Cyber. 4, 427–444 (2013). https://doi.org/10.1007/s13042-012-0105-9

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