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Cited by 3,502

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10.1162/153244303322753616

·2000·CrossRef Listing of Deleted DOIs
DOI 10.1162/153244303322753616
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Abstract

Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.

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Research works printed but not linked: 44

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Cited by

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Metadata

Venue
CrossRef Listing of Deleted DOIs
Publisher
Unmaintained records
Published
2000
Volume
1
ISSN
0003-6951 (ISSN-L)
DOI
10.1162/153244303322753616
OpenAlex
W2119479037
Cited by
3,502
References
0
FWCI
2.25
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.