Evaluation Method for Decision Rule Sets

Authors:
Yuhua Qian;Jiye Liang
Affiliations:
Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, People' ...;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, People' ...
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

Citing 5
Cited 1

Uncertainly measures of rough set prediction

Artificial Intelligence
Various approaches to reasoning with frequency based decision reducts: a survey

Rough set methods and applications
Induction of Classification Rules by Granular Computing

TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A roughness measure for fuzzy sets

Information Sciences: an International Journal
Combination entropy and combination granulation in incomplete information system

RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology

Consistency measure, inclusion degree and fuzzy measure in decision tables

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a decision table in rough set theory is classified into three types according to its consistency. Three parameters ï戮驴(whole certainty measure), β(whole consistency measure) and ï戮驴(whole support measure) are introduced to evaluate the performance of a decision rule set induced from a decision table. For three types of decision tables, the dependency of the parameters upon condition/decision granulation is analyzed. The parameters can be used to construct an evaluation function in favor of selecting a better one from some different rule acquiring methods for real decision problems.