DocumentCode :
532274
Title :
Measurement of attribute classification power based on derivative information
Author :
Fan, Yanfeng ; Yang, Zhixiao ; Cheng, Xingxing ; Zhang, Dexian
Author_Institution :
Coll. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China
Volume :
3
fYear :
2010
fDate :
22-24 Oct. 2010
Abstract :
Attribute importance ranking still is a key problem required to be solved for the classification problem. The lack of efficient heuristic information is the fundamental reason that affects the attribute selection in data mining. In this paper, to determining the importance level of the attributes, a new measure based on partial derivative distribution of the classification hypersurface output corresponding to the input attributes is proposed. This paper indicates that if the classification hypersurface is acquired by SVM, it is more convenient to measure the attribute importance ranking. The validity of the proposed method is experimentally evaluated, the experimental results prove that the approach is more efficient.
Keywords :
data mining; pattern classification; support vector machines; SVM; attribute classification power measurement; attribute importance ranking; classification hypersurface output; data mining; derivative information; partial derivative distribution; attribute importance ranking; attribute selection; classification hypersurface; partial derivative distribution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
Type :
conf
DOI :
10.1109/ICCASM.2010.5620256
Filename :
5620256
Link To Document :
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