An interval set model for learning rules from incomplete information table

  • Authors:

  • Huaxiong Li;Minhong Wang;Xianzhong Zhou;Jiabao Zhao

  • Affiliations:

  • School of Management and Engineering, Nanjing University, Nanjing 210093, Jiangsu, PR China and State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, Jiangsu, PR ...;Faculty of Education, The University of Hong Kong, Hong Kong;School of Management and Engineering, Nanjing University, Nanjing 210093, Jiangsu, PR China;School of Management and Engineering, Nanjing University, Nanjing 210093, Jiangsu, PR China

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2012

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Abstract

A novel interval set approach is proposed in this paper to induce classification rules from incomplete information table, in which an interval-set-based model to represent the uncertain concepts is presented. The extensions of the concepts in incomplete information table are represented by interval sets, which regulate the upper and lower bounds of the uncertain concepts. Interval set operations are discussed, and the connectives of concepts are represented by the operations on interval sets. Certain inclusion, possible inclusion, and weak inclusion relations between interval sets are presented, which are introduced to induce strong rules and weak rules from incomplete information table. The related properties of the inclusion relations are proved. It is concluded that the strong rules are always true whatever the missing values may be, while the weak rules may be true when missing values are replaced by some certain known values. Moreover, a confidence function is defined to evaluate the weak rule. The proposed approach presents a new view on rule induction from incomplete data based on interval set.