A Multiple-category Classification Approach with Decision-theoretic Rough Sets

  • Authors:

  • Dun Liu;Tianrui Li;Huaxiong Li

  • Affiliations:

  • (Correspd.) (Supported by the Major Program of Natnl. Natural Sci. Fndn. of China (No. 71090402/G1), the Natnl. Sci. Fndn. of China (Nos. 61175047, 60873108, 70971062), the Youth Social Sci. Fndn. ...;School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, P.R. China, trli@swjtu.edu.cn;School of Management and Engineering, Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, P.R. China, huaxiongli@nju.edu.cn

  • Venue:
  • Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
  • Year:
  • 2012

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Abstract

By considering the levels of tolerance for errors and the cost of actions in real decision procedure, a new two-stage approach is proposed to solve the multiple-category classification problems with Decision-Theoretic Rough Sets (DTRS). The first stage is to change an m-category classification problem (m 2) into an m two-category classification problem, and form three types of decision regions: positive region, boundary region and negative region with different states and actions by using DTRS. The positive region makes a decision of acceptance, the negative region makes a decision of rejection, and the boundary region makes a decision of abstaining. The second stage is to choose the best candidate classification in the positive region by using the minimum probability error criterion with Bayesian discriminant analysis approach. A case study of medical diagnosis demonstrates the proposed method.