Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tpami.2005.159

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 30

  1. 10.1038/35000501 · DOI · Paper page
  2. Burges, 1998, A Tutorial on Support Vector Machines for Pattern Recognition, Knowledge Discovery and Data Mining, 2, 1, 10.1023/A:1009715923555 · DOI · Paper page
  3. 10.1109/TSMC.1974.5408535 · DOI
  4. 10.1109/CSB.2003.1227396 · DOI · Paper page
  5. 10.1007/3-540-45014-9_2 · DOI
  6. Hadley, Registration of Localization Images by Maximization of Mutual Information
  7. 10.1109/TMI.2004.826949 · DOI
  8. Hsu, 2002, A Comparison of Methods for Multi-Class Support Vector Machines, IEEE Trans. Neural Networks, 13, 415, 10.1109/72.991427 · DOI · Paper page
  9. 10.1002/0471221317 · DOI · Paper page
  10. 10.1109/TPAMI.2003.1201827 · DOI
  11. 10.1142/9789812776303_0006 · DOI
  12. 10.1109/34.574797 · DOI · Paper page
  13. 10.1109/34.824819 · DOI · Paper page
  14. 10.1016/S0004-3702(97)00043-X · DOI · Paper page
  15. Krantz, 1999, Jensen’s Inequality, 118
  16. 10.1109/TPAMI.2002.1114861 · DOI · Paper page
  17. 10.21236/ADA292575 · DOI · Paper page
  18. Li, How Many Genes Are Needed for a Discriminant Microarray Data Analysis?, 137
  19. Mitchell, 1997, Machine Learning
  20. 10.1214/aoms/1177704472 · DOI · Paper page
  21. Peng, 1997, Two Optimization Criterions for Neural Networks and Their Applications in Unconstrained Character Recognition, J. Circuits and Systems, 2, 1
  22. 10.1109/ISBI.2002.1029399 · DOI
  23. 10.1109/ICDM.2003.1250992 · DOI
  24. 10.1016/0167-8655(94)90127-9 · DOI · Paper page
  25. 10.1038/73432 · DOI · Paper page
  26. 10.1038/73439 · DOI · Paper page
  27. 2005, UCI Learning Repository
  28. Webb, 1999, Statistical Pattern Recognition
  29. Xing, Feature Selection for High-Dimensional Genomic Microarray Data
  30. 10.1101/gr.190001 · DOI · Paper page

Cited by

Items 101–109 of 109

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)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

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.