Measures for evaluating the decision performance of a decision table in rough set theory

  • Authors:

  • Yuhua Qian;Jiye Liang;Deyu Li;Haiyun Zhang;Chuangyin Dang

  • Affiliations:

  • Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, Taiyuan 030006, China and School of Computer and Information Technology Shanxi University ...;Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, Taiyuan 030006, China and School of Computer and Information Technology Shanxi University ...;Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, Taiyuan 030006, China and School of Computer and Information Technology Shanxi University ...;Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, Taiyuan 030006, China and School of Computer and Information Technology Shanxi University ...;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

Quantified Score

Hi-index 0.07

Visualization

Abstract

As two classical measures, approximation accuracy and consistency degree can be employed to evaluate the decision performance of a decision table. However, these two measures cannot give elaborate depictions of the certainty and consistency of a decision table when their values are equal to zero. To overcome this shortcoming, we first classify decision tables in rough set theory into three types according to their consistency and introduce three new measures for evaluating the decision performance of a decision-rule set extracted from a decision table. We then analyze how each of these three measures depends on the condition granulation and decision granulation of each of the three types of decision tables. Experimental analyses on three practical data sets show that the three new measures appear to be well suited for evaluating the decision performance of a decision-rule set and are much better than the two classical measures.