Variable Ranking for Online Ensemble Learning

In proposing, incremental feature selection based on correlation ranking (CR) for classification problems, we develop on-line training using the random forests (RF) algorithm, then evaluate the performance of the combination based on an NIPS 2003 Feature Selection Challenge dataset. Results show tha...

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Veröffentlicht in:Journal of advanced computational intelligence and intelligent informatics Jg. 13; H. 3; S. 331 - 337
1. Verfasser: Osman, Hassab Elgawi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 20.05.2009
ISSN:1343-0130, 1883-8014
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Abstract In proposing, incremental feature selection based on correlation ranking (CR) for classification problems, we develop on-line training using the random forests (RF) algorithm, then evaluate the performance of the combination based on an NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.
AbstractList In proposing, incremental feature selection based on correlation ranking (CR) for classification problems, we develop on-line training using the random forests (RF) algorithm, then evaluate the performance of the combination based on an NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.
Author Osman, Hassab Elgawi
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Cites_doi 10.1007/978-3-540-25966-4_22
10.1007/BF00058655
10.1214/aos/1024691352
10.1016/S0004-3702(97)00043-X
10.1023/A:1010933404324
10.1109/CVPRW.2008.4563065
10.1016/j.artint.2004.05.009
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CorporateAuthor Image Science and Engineering Lab, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
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