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http://dx.doi.org/10.14400/JDC.2014.12.6.223

Rough Entropy-based Knowledge Reduction using Rough Set Theory  

Park, In-Kyoo (Dept. of Computer Science Joongbu University)
Publication Information
Journal of Digital Convergence / v.12, no.6, 2014 , pp. 223-229 More about this Journal
Abstract
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.
Keywords
Data Mining; Rough Set; Feature Selection; Quick-Reduct; Rough Entropy;
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1 M. Dashand and H Liu, "Feature selection for classification", Intelligent Data Analysis, Vol. 1, No. 3, pp. 131-156, 1997.   DOI   ScienceOn
2 M. Dash and H. Liu, "Unsupervised feature selction", in Proc. of the Pacific and Asia Conf. on Knowledge Discovery and Data Mining, Kyoto, pp. 110-121, 2000.
3 C. Velayutham and K. Thangaval, "Unsupervised Quick Reduct Algorithm using Rough Set Theory", Jouranl of Electronic Seience and Technology Vol. 9, No. 3, pp. 193-201, 2011.
4 S. K. Das, "Feature selection with a linear dependence measure", IEEE Trans. on Computers, Vol. 20, No. 9, pp. 1106-1109, 1971.
5 Lin Sun, "Decision Table Reduction Method Based on New Conditional Entropy for Rough Set theory", International Workshop on Intelligent Systems and Applications, pp. 23-24, May 2009
6 Baoxiang Liu, Ying Li, Lihong Li, Yaping Yu, "An Approximate Reduction Algorithm Based on Conditional Entropy", Information Computing and Applications, Vol. 106, pp. 319-32, 2010   DOI
7 Zhangyan Xu, Jianhua Zhou, Chenguang Zhang, "A Quick Attribute Reduction Algorithm Based on Incomplete Decision Table", Information Computing and Applications, Vol. 391, pp. 499-508, 2013   DOI
8 K. Thankaveland A. Pethalakshmi, "Dimensionality reduction based on rough set theory: a Review", Applied Soft Computing, Vol. 9, No. 1, pp. 1-12, 2009.   DOI   ScienceOn
9 J. Han, X. Hu and T.-Y. Lin, "Feature sebset selection based on relative dependency between attributes", in Proc. of the 4th International Conf. on Rough Sets and Current Trends in Computing, Uppsala, pp. 176-185, 2004.
10 J. W. Grzymala-Busse, "LERS-a System for learning from examples based on rough sets", in Intellegent Decision Support, Kruwer Academic Publishers, pp. 3-18, 1992.