(Enter summary)
Abstract: The traditional motivation behind feature selection algorithms
is to find the best subset of features for a task
using one particular learning algorithm. Given the
recent success of ensembles, however, we investigate
the notion of ensemble feature selection in this paper.
This task is harder than traditional feature selection
in that one not only needs to find features germane to
the learning task and learning algorithm, but one also
needs to find a set of feature subsets that will promote... (Update)
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BibTeX entry: (Update)
Opitz, D. submitted. Feature selection for ensembles. In Proceedings of the Sixteenth National Conference on Artificial Intelligence. Randic, M. 1975. On characterization of molecular branching. Journal of American Chemical Society 97:6609--6615. http://citeseer.ist.psu.edu/opitz99feature.html More
@inproceedings{ opitz99feature,
author = "David Opitz",
title = "Feature Selection for Ensembles",
booktitle = "{AAAI}/{IAAI}",
pages = "379-384",
year = "1999",
url = "citeseer.ist.psu.edu/opitz99feature.html" }
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