Feature Selection in Statistical Classification
DOI:
https://doi.org/10.6000/1929-6029.2012.01.02.11Keywords:
Statistical classification, supervised statistical learning, machine learning, curse of dimensionality, over-fitting, feature selection, filter, ranker, wrapper, embedded methodsAbstract
We give a brief overview of feature selection methods used in statistical classification. We cover filter, wrapper and embedded methods.
References
Hastie T., Tibshirani R., and Friedman J. The elements of statistical learning. 2nd edition. Springer 2009.http://dx.doi.org/10.1007/978-0-387-84858-7 DOI: https://doi.org/10.1007/978-0-387-84858-7
Kohl M. Performance measures in binary classification. Int J Stats Med Res 2012 Sep; 1(1): 79-81. DOI: https://doi.org/10.6000/1929-6029.2012.01.01.08
Guyon I. and Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 2003 Mar; 1157–1182, Mar 2003.
Navot A., Gilad-Bachrach R., Navot Y. and Tishby N. mIs Feature Selection Still Necessary? In: Saunders C., Grobelnik M., Gunn S., and Shawe-Taylor J., editors. mSubspace, Latent Structure and Feature Selection. Lecture Notes in Computer Science, volume 3940; Springer 2006: p. 127-138. DOI: https://doi.org/10.1007/11752790_8
Bourgon R., Gentleman R., and Huber, W. Independent filtering increases detection power for high-throughput experiments. Proc. Natl. Acad. Sci. U.S.A. 2010 May; 107(21): 9546-51.http://dx.doi.org/10.1073/pnas.0914005107 DOI: https://doi.org/10.1073/pnas.0914005107
Koller D. and Sahami M. Toward optimal feature selection. In: 13th International Conference on Machine Learning, July 1996: p. 284-292.
Hall M.A. and Holmes G. Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Know. Data Eng. 2003 May/Jun; 15(3): 1437-47. http://dx.doi.org/10.1109/TKDE.2003.1245283 DOI: https://doi.org/10.1109/TKDE.2003.1245283
Pochet N., De Smet F., Suykens J.A.K., and De Moor B.L.R. Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinformatics 2004 Nov; 20(17): 3185-95. http://dx.doi.org/10.1093/bioinformatics/bth383 DOI: https://doi.org/10.1093/bioinformatics/bth383
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2012 Matthias Kohl
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Policy for Journals/Articles with Open Access
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
Policy for Journals / Manuscript with Paid Access
Authors who publish with this journal agree to the following terms:
- Publisher retain copyright .
- Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work .