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Cited by 10,544

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

Hanchuan Peng, Fuhui Long, Ding, C.·2005·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2005.159
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Abstract

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.

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Cited by

Metadata

Authors
Peng, H., Long, F., & Ding, C.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2005-08
Volume
27
Issue
8
Pages
1226-1238
ISSN
0162-8828 (ISSN-L)
DOI
10.1109/tpami.2005.159
OpenAlex
W2154053567
Cited by
10,544
References
2
FWCI
71.46
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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