Rule Extraction Algorithm Based on Discernibility Matrix in Inconsistent Decision Table
Wenbin Qian, Yang Bing-ru, Zhangyan Xu, Yonghong Xie
Abstract
Wenbin Qian, Yang Bing-ru, Zhangyan Xu, Yonghong Xie
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.
OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
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