2013•Unpublished venueRequires access

Rule Extraction Algorithm Based on Discernibility Matrix in Inconsistent Decision Table

Wenbin Qian, Yang Bing-ru, Zhangyan Xu, Yonghong Xie

Open publisher page 2 citations

Abstract

Since the efficiency of traditional rule extraction algorithms based on discernibility matrix in inconsistent decision table is often poor,a quick rule extraction algorithm based on discernibility matrix was proposed to deal with the problem.The definite of simplified decision table is first introduced,and many duplicate objects are deleted in decision table.Then the subsets of discernibility matrix is constructed with respect to different decision classes,which effectively avoids the imbalance of objects and compresses the storage space of algorithm,and adopting the heuristic search strategy with backward greedy to calculate the relative minimal attribute reduction.Some useful decion rules based on reliabi-lity are extracted,what's more,the reliability is dynamically given,and the algorithm has good adaptability.Finally,example analysis and experiential results show that the proposed algorithm can exact effective decision rules from inconsistent decision table.

About this research paper

What this paper is about

Since the efficiency of traditional rule extraction algorithms based on discernibility matrix in inconsistent decision table is often poor,a quick rule extraction algorithm based on discernibility matrix was proposed to deal with the problem.The definite of simplified decision table is first introduced,and many duplicate objects are deleted in decision table.Then the subsets of discernibility matrix is constructed with respect to different decision classes,which effectively avoids the imbalance of objects and compresses the storage space of algorithm,and adopting the heuristic search strategy with backward greedy to calculate the relative minimal attribute reduction.Some useful decion rules based on reliabi-lity are extracted,what's more,the reliability is dynamically given,and the algorithm has good adaptability.Finally,example analysis and experiential results show that the proposed algorithm can exact effective decision rules from inconsistent decision table.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Since the efficiency of traditional rule extraction algorithms based on discernibility matrix in inconsistent decision table is often poor,a quick rule extraction algorithm based on discernibility matrix was proposed to deal with the problem.The definite of simplified decision table is first introduced,and many duplicate objects are deleted in decision table.Then the subsets of discernibility matrix is constructed with respect to different decision classes,which effectively avoids the imbalance of objects and compresses the storage space of algorithm,and adopting the heuristic search strategy with backward greedy to calculate the relative minimal attribute reduction.Some useful decion rules based on reliabi-lity are extracted,what's more,the reliability is dynamically given,and the algorithm has good adaptability.Finally,example analysis and experiential results show that the proposed algorithm can exact effective decision rules from inconsistent decision table.

Key concepts: Decision table, Computer science, Table (database), Algorithm, Matrix (chemical analysis), Decision matrix, Greedy algorithm, Heuristic

Related papers

Back to paper searchBrowse research topicsOriginal source
Rule Extraction Algorithm Based on Discernibility Matrix in Inconsistent Decision Table — Research Paper | ScholarLens