Article

Redundancy based feature selection for microarray data

Authors:

Huan LiuAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 737 - 742

https://doi.org/10.1145/1014052.1014149

Published: 22 August 2004 Publication History

Abstract

In gene expression microarray data analysis, selecting a small number of discriminative genes from thousands of genes is an important problem for accurate classification of diseases or phenotypes. The problem becomes particularly challenging due to the large number of features (genes) and small sample size. Traditional gene selection methods often select the top-ranked genes according to their individual discriminative power without handling the high degree of redundancy among the genes. Latest research shows that removing redundant genes among selected ones can achieve a better representation of the characteristics of the targeted phenotypes and lead to improved classification accuracy. Hence, we study in this paper the relationship between feature relevance and redundancy and propose an efficient method that can effectively remove redundant genes. The efficiency and effectiveness of our method in comparison with representative methods has been demonstrated through an empirical study using public microarray data sets.

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Cited By

Espinosa RJiménez FPalma J(2024)Surrogate-Assisted and Filter-Based Multiobjective Evolutionary Feature Selection for Deep LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.323462935:7(9591-9605)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2023.3234629
Combrisson EDi Rienzo FSaive APerrone-Bertolotti MSoto JKahane PLachaux JGuillot AJerbi K(2024)Human local field potentials in motor and non-motor brain areas encode upcoming movement directionCommunications Biology10.1038/s42003-024-06151-37:1Online publication date: 27-Apr-2024
https://doi.org/10.1038/s42003-024-06151-3
Solorio-Fernández SCarrasco-Ochoa JMartínez-Trinidad J(2024)Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measureNeurocomputing10.1016/j.neucom.2023.127111571(127111)Online publication date: Feb-2024
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Index Terms

Redundancy based feature selection for microarray data
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 August 2004

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KDD04: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 22 - 25, 2004

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Espinosa RJiménez FPalma J(2024)Surrogate-Assisted and Filter-Based Multiobjective Evolutionary Feature Selection for Deep LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.323462935:7(9591-9605)Online publication date: Jul-2024
https://doi.org/10.1109/TNNLS.2023.3234629
Combrisson EDi Rienzo FSaive APerrone-Bertolotti MSoto JKahane PLachaux JGuillot AJerbi K(2024)Human local field potentials in motor and non-motor brain areas encode upcoming movement directionCommunications Biology10.1038/s42003-024-06151-37:1Online publication date: 27-Apr-2024
https://doi.org/10.1038/s42003-024-06151-3
Solorio-Fernández SCarrasco-Ochoa JMartínez-Trinidad J(2024)Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measureNeurocomputing10.1016/j.neucom.2023.127111571(127111)Online publication date: Feb-2024
https://doi.org/10.1016/j.neucom.2023.127111
Sidhom OGhazouani HBarhoumi W(2024)Three-phases hybrid feature selection for facial expression recognitionThe Journal of Supercomputing10.1007/s11227-023-05758-380:6(8094-8128)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11227-023-05758-3
Gu XChen JWu GWang KWang J(2024)Feature subset selection algorithm based on symmetric uncertainty and interaction factorMultimedia Tools and Applications10.1007/s11042-023-15821-z83:4(11247-11260)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15821-z
Theng DBhoyar K(2024)Feature selection techniques for machine learning: a survey of more than two decades of researchKnowledge and Information Systems10.1007/s10115-023-02010-566:3(1575-1637)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s10115-023-02010-5
Alcan V(2024)Enhancing age-related postural sway classification using partial least squares-discriminant analysis and hybrid feature setNeural Computing and Applications10.1007/s00521-024-09557-636:10(5621-5643)Online publication date: 1-Apr-2024
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BÜYÜKKEÇECİ MOKUR M(2023)A Comprehensive Review of Feature Selection and Feature Selection Stability in Machine LearningGazi University Journal of Science10.35378/gujs.99376336:4(1506-1520)Online publication date: 1-Dec-2023
https://doi.org/10.35378/gujs.993763
Swapnesh Kumar Nayak DDas JSwarnkar T(2023)Deep Learning Techniques for Biomedical Research and Significant Gene Identification using Next Generation Sequencing (NGS) Data: - A ReviewData Science and Interdisciplinary Research: Recent Trends and Applications10.2174/9789815079005123050011(172-216)Online publication date: 25-Sep-2023
https://doi.org/10.2174/9789815079005123050011
Gao HYuan YWang W(2023)RETRACTED ARTICLE: Features optimization selection in hidden layers of deep learning based on graph clusteringEURASIP Journal on Wireless Communications and Networking10.1186/s13638-023-02292-x2023:1Online publication date: 21-Aug-2023
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