Abstract
Exploiting the diversity of hypotheses produced by evolutionary learning, a new ensemble approach for Feature Selection is presented, aggregating the feature rankings extracted from the hypotheses. A statistical model is devised to enable the direct evaluation of the approach; comparative experimental results show its good behavior on non-linear concepts when the features outnumber the examples. © Springer-Verlag 2004.
Cite
CITATION STYLE
Jong, K., Marchiori, E., & Sebag, M. (2004). Ensemble learning with evolutionary computation: Application to feature ranking. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3242, 1133–1142. https://doi.org/10.1007/978-3-540-30217-9_114
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