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 |
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| 1. Verfasser: | |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
20.05.2009
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| ISSN: | 1343-0130, 1883-8014 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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. |
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| ISSN: | 1343-0130 1883-8014 |
| DOI: | 10.20965/jaciii.2009.p0331 |