Computer Science ›› 2019, Vol. 46 ›› Issue (10): 236-241.doi: 10.11896/jsjkx.190200270

• Artificial Intelligence • Previous Articles     Next Articles

Formal Vector Method of Rule Extraction for Consistent Decision Information System

YAN An1, YAN Xin-yi1, CHEN Ze-hua2   

  1. (College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan 030024,China)1
    (College of Data Science,Taiyuan University of Technology,Taiyuan 030024,China)2
  • Received:2019-02-11 Revised:2019-05-15 Online:2019-10-15 Published:2019-10-21

Abstract: Knowledge representation and acquisition is one of the key problems in the field of artificial intelligence,and rule acquisition is one of the important research contents.Formal concept analysis is an effective method to deal with big data and uncertain knowledge,which is widely used in knowledge representation and data mining.Formal concept analysis can realize the rule extraction of decision information system.Firstly,the decision information system is transformed into the formal context,then the concept is generated by the formal context and the rules are acquired by concept operation.However,the generation of concepts is a complex computational process,and the generated rules are often redundant.On the basis of the formal context,the formal vector and its properties were defined and discussed,the tree topology diagram of formal vector was constructed,and a rule extraction algorithm of the decision information system based on the formal vector was proposed.The algorithm is based on granular computing,the formal vector in each layer from coarse to fine granularity space is computed,and the rules are extracted by the relationship between the conditional formal vectors and the decision formal vectors.The visualization of the rule extraction process is realized based on the tree topology diagram.And the actual time cost of the rule extraction process is greatly reduced by pruning operation.Finally,the correctness and effectiveness of the algorithm were verified by mathematical proof and case analysis.The comparison experiment also shows that the algorithm has better timeliness and higher recognition rate at the same time.

Key words: Decision information system, Formal context, Formal vector, Granular computing, Rule acquisition

CLC Number: 

  • TP181
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