The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
A review of feature selection methods based on mutual information
Open accessAbstract
In this work, we present a review of the state of the art of information-theoretic feature selection methods. The concepts of feature relevance, redundance, and complementarity (synergy) are clearly defined, as well as Markov blanket. The problem of optimal feature selection is defined. A unifying theoretical framework is described, which can retrofit successful heuristic criteria, indicating the approximations made by each method. A number of open problems in the field are presented.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 48,949
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,544
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,715
Information theory and statistics
(1959) · Journal of the Franklin Institute · doi:10.1016/0016-0032(59)90368-0 · Cited by 7,094
A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., & Larrañaga, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm344 · Cited by 5,244
Cited by
Feature selection using Joint Mutual Information Maximisation
Bennasar, M., Hicks, Y., & Setchi, R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.007 · Cited by 659
Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma
Beig, N., Patel, J., Prasanna, P., Hill, V., Gupta, A., Correa, R., Bera, K., Singh, S., Partovi, S., Varadan, V., et al. (2018) · Scientific Reports · doi:10.1038/s41598-017-18310-0 · Cited by 157
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Jia, Y., Hu, X., Kang, W., & Dong, X. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.3c09653 · Cited by 111
Distributed selection of continuous features in multilabel classification using mutual information
Gonzalez-Lopez, J., Ventura, S., & Cano, A. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944298 · Cited by 60
Al-Hameed, S., Benaissa, M., Christensen, H., Mirheidari, B., Blackburn, D., & Reuber, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217388 · Cited by 37
Metadata
- Authors
- Vergara, J. R., & Estévez, P. A.
- Venue
- Neural Computing and Applications
- Published
- 2013-03-13
- Volume
- 24
- Issue
- 1
- Pages
- 175-186
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W2118561568
- Cited by
- 1,255
- References
- 60
- FWCI
- 22.82
- Type
- journal article
- Language
- en
- Open access
- green
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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.
