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2010年09月02日 ICICTA2010被EI 检索！！！ 已有7人参与
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2010年09月02日 ICICTA2010被EI 检索！！！ 2010年09月02日 ICICTA2010被EI 检索！！！ 2010年09月02日 ICICTA2010被EI 检索！！！ 2010年09月02日 ICICTA2010被EI 检索！！！ |
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8楼2010-09-02 16:43:27
我心永恒1567
木虫 (正式写手)
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4楼2010-09-02 13:30:21
6楼2010-09-02 16:12:39
我心永恒1567
木虫 (正式写手)
- 应助: 0 (幼儿园)
- 金币: 3975.3
- 散金: 5
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小木虫(金币+0.5):给个红包，谢谢回帖交流
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Accession number: 20103413169767 Title: Feature selection through optimization of k-nearest neighbor matching gain Authors: Luo, Yihui1 ; Xiong, Shuchu1 Author affiliation: 1 Department of Information, Hunan University of Commerce, Changsha, China Corresponding author: Luo, Y. (yihuiluo@yahoo.com.cn) Source title: 2010 International Conference on Intelligent Computation Technology and Automation, ICICTA 2010 Abbreviated source title: Int. Conf. Intelligent Comput. Technol. Autom., ICICTA Volume: 2 Monograph title: 2010 International Conference on Intelligent Computation Technology and Automation, ICICTA 2010 Issue date: 2010 Publication year: 2010 Pages: 309-312 Article number: 5522419 Language: English ISBN-13: 9780769540771 Document type: Conference article (CA) Conference name: 2010 International Conference on Intelligent Computation Technology and Automation, ICICTA 2010 Conference date: May 11, 2010 - May 12, 2010 Conference location: Changsha, China Conference code: 81471 Sponsor: IEEE Intelligent Computation Society; Res. Assoc. Intelligent Comput. Technol. Autom.; Hunan University; Changsha University of Science and Technology; Hunan University of Science and Technology Publisher: IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States Abstract: Many problems in information processing involve some form of dimensionality reduction. In this paper, we propose a new model for feature evaluation and selection in unsupervised learning scenarios. The model makes no special assumptions on the nature of the data set. For each of the data set, the original features induce a ranking list of items in its k nearest neighbors. The evaluation criterion favors reduced features that result in the most consistent to these ranked lists. And an efficiently local descent search based on the model is adopted to select the reduced features. Our experiments with several data sets demonstrate that the proposed algorithm is able to detect completely irrelevant features and to remove some additional features without significantly hurting the performance of the clustering algorithm. © 2010 IEEE. Number of references: 11 Main heading: Feature extraction Controlled terms: Clustering algorithms - Data processing - Unsupervised learning Uncontrolled terms: Data sets - Dimensionality reduction - Evaluation criteria - Feature evaluation and selection - Feature selection - Information processing - K-nearest neighbors - New model - Search-based Classification code: 716 Telecommunication; Radar, Radio and Television - 721 Computer Circuits and Logic Elements - 723 Computer Software, Data Handling and Applications DOI: 10.1109/ICICTA.2010.608 Database: Compendex Compilation and indexing terms, © 2010 Elsevier Inc. |
7楼2010-09-02 16:42:14
