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JACIII Vol.13 No.3 pp. 331-337
doi: 10.20965/jaciii.2009.p0331
(2009)

Paper:

Variable Ranking for Online Ensemble Learning

Hassab Elgawi Osman

Image Science and Engineering Lab, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

Received:
September 26, 2008
Accepted:
February 10, 2009
Published:
May 20, 2009
Keywords:
ensemble learning, on-line learning, feature selection, random forests, NIPS 2003.
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.

Cite this article as:
Hassab Elgawi Osman, “Variable Ranking for Online Ensemble Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.3, pp. 331-337, 2009.
Data files:
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Last updated on Oct. 15, 2021