Rough Entropy-based Knowledge Reduction using Rough Set Theory

러프집합 이론을 이용한 러프 엔트로피 기반 지식감축

  • 박인규 (중부대학교 컴퓨터학과)
  • Received : 2014.03.03
  • Accepted : 2014.06.20
  • Published : 2014.06.28


In an attempt to retrieve useful information for an efficient decision in the large knowledge system, it is generally necessary and important for a refined feature selection. Rough set has difficulty in generating optimal reducts and classifying boundary objects. In this paper, we propose quick reduction algorithm generating optimal features by rough entropy analysis for condition and decision attributes to improve these restrictions. We define a new conditional information entropy for efficient feature extraction and describe procedure of feature selection to classify the significance of features. Through the simulation of 5 datasets from UCI storage, we compare our feature selection approach based on rough set theory with the other selection theories. As the result, our modeling method is more efficient than the previous theories in classification accuracy for feature selection.


Data Mining;Rough Set;Feature Selection;Quick-Reduct;Rough Entropy


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