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Feature selection: evaluation, application, and small sample performance
Abstract
A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. We also illustrate the dangers of using feature selection in small sample size situations.
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Cited by
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Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334
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Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.010 · Cited by 2,258
“Different feature selection methods can be used for this purpose and may exploit machine learning or statistical approaches [72–76].”
MethodHistopathological Image Analysis: A Review
Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052
A review of feature selection methods on synthetic data
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0487-8 · Cited by 830
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Metadata
- Authors
- Jain, A., & Zongker, D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1997
- Volume
- 19
- Issue
- 2
- Pages
- 153-158
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2148633389
- Cited by
- 2,171
- References
- 0
- FWCI
- 104.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jain, A., & Zongker, D. (1997). Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.574797
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
