NDLI: An Adaptive Optimization Method of Configuring Feature Weight Group

Content Provider	IEEE Xplore Digital Library
Author	Xin-Quan Chen Hong Peng Jing-Song Hu
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou (Xin-Quan Chen; Hong Peng; Jing-Song Hu)
Abstract	It introduces a blended objective function that can represent the guideline more precisely, data points in any cluster are close to each other, and data points between any two clusters are away from each other. By optimizing the blended objective function to get an optimal feature weight group, we can construct a classifier based on clustering with an optimal distance measure. In order to seek an acceptable solution of the blended objective function, it gives an adaptive optimization method of configuring feature weight group based on reverse projection of grads of the blended objective function. The method is valid by experiments of two data sets from UCI. At last, it gives some analysis and discussions of the method and points out that it can be applied to continuous attributes reduction
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	1281
Ending Page	1286
File Size	267104
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258653
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization methods Principal component analysis Computer science Data engineering Guidelines Linear discriminant analysis Rough sets Weight measurement World Wide Web Data analysis adaptive optimization method configuring feature weight group Optimal distance measure classifier based on clustering reverse projection of grads
Content Type	Text
Resource Type	Article

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