Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification

Junshan Yang; Zexuan Zhu; Shan He; Zhen Ji

doi:10.1109/CIBCB.2013.6595417

Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification

Junshan Yang, Zexuan Zhu, Shan He, Zhen Ji^*

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Omics refers to a field of study in biology such as genomics, proteomics, and metabolomics. Investigating fundamental biological problems based on omics data would increase our understanding of bio-systems as a whole. However, omics data is characterized with high-dimensionality and unbalance between features and samples, which poses big challenges for classical statistical analysis and machine learning methods. This paper studies a minimal-redundancy-maximal- relevance (MRMR) feature selection for omics data classification using three different relevance evaluation measures including mutual information (MI), correlation coefficient (CC), and maximal information coefficient (MIC). A linear forward search method is used to search the optimal feature subset. The experimental results on five real-world omics datasets indicate that MRMR feature selection with CC is more robust to obtain better (or competitive) classification accuracy than the other two measures.

Original language	English
Title of host publication	Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Pages	246-251
Number of pages	6
DOIs	https://doi.org/10.1109/CIBCB.2013.6595417
Publication status	Published - 2013
Event	10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 - Singapore, Singapore Duration: 16 Apr 2013 → 19 Apr 2013

Publication series

Name	Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Conference

Conference	10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Country/Territory	Singapore
City	Singapore
Period	16/04/13 → 19/04/13

Bibliographical note

Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Biomedical Engineering

Access to Document

10.1109/CIBCB.2013.6595417

Cite this

Yang, J., Zhu, Z., He, S., & Ji, Z. (2013). Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 (pp. 246-251). Article 6595417 (Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). https://doi.org/10.1109/CIBCB.2013.6595417

Yang, Junshan ; Zhu, Zexuan ; He, Shan et al. / Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. 2013. pp. 246-251 (Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).

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title = "Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification",

abstract = "Omics refers to a field of study in biology such as genomics, proteomics, and metabolomics. Investigating fundamental biological problems based on omics data would increase our understanding of bio-systems as a whole. However, omics data is characterized with high-dimensionality and unbalance between features and samples, which poses big challenges for classical statistical analysis and machine learning methods. This paper studies a minimal-redundancy-maximal- relevance (MRMR) feature selection for omics data classification using three different relevance evaluation measures including mutual information (MI), correlation coefficient (CC), and maximal information coefficient (MIC). A linear forward search method is used to search the optimal feature subset. The experimental results on five real-world omics datasets indicate that MRMR feature selection with CC is more robust to obtain better (or competitive) classification accuracy than the other two measures.",

author = "Junshan Yang and Zexuan Zhu and Shan He and Zhen Ji",

note = "Copyright: Copyright 2013 Elsevier B.V., All rights reserved.; 10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 ; Conference date: 16-04-2013 Through 19-04-2013",

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Yang, J, Zhu, Z, He, S & Ji, Z 2013, Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification. in Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013., 6595417, Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, pp. 246-251, 10th Annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, Singapore, 16/04/13. https://doi.org/10.1109/CIBCB.2013.6595417

Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification. / Yang, Junshan; Zhu, Zexuan; He, Shan et al.
Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. 2013. p. 246-251 6595417 (Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - He, Shan

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AB - Omics refers to a field of study in biology such as genomics, proteomics, and metabolomics. Investigating fundamental biological problems based on omics data would increase our understanding of bio-systems as a whole. However, omics data is characterized with high-dimensionality and unbalance between features and samples, which poses big challenges for classical statistical analysis and machine learning methods. This paper studies a minimal-redundancy-maximal- relevance (MRMR) feature selection for omics data classification using three different relevance evaluation measures including mutual information (MI), correlation coefficient (CC), and maximal information coefficient (MIC). A linear forward search method is used to search the optimal feature subset. The experimental results on five real-world omics datasets indicate that MRMR feature selection with CC is more robust to obtain better (or competitive) classification accuracy than the other two measures.

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Yang J, Zhu Z, He S, Ji Z. Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. 2013. p. 246-251. 6595417. (Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013). doi: 10.1109/CIBCB.2013.6595417

Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification

Abstract

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Bibliographical note

ASJC Scopus subject areas

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