research-article

Classifying microarray data with association rules

Authors:

Kyrylo BessonovAuthors Info & Claims

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

Pages 94 - 99

https://doi.org/10.1145/1982185.1982209

Published: 21 March 2011 Publication History

Abstract

In this paper we investigate a method for classifying microarray data using association rules. Associative classifiers, classification systems based on association rules, show good performance level while being easy to read and understand. This feature is especially attractive for biological data where experts can read and validate the association rules. Relevant features are selected using Support Vector Machines with Recursive Feature Elimination. These features are discretized according to their relative expression levels (upregulated, downregulated or no change) and then they are used to build an associative classifier. The proposed combination proves highly accurate for the studied microarray data collection. In addition the classification rules discovered and employed in the classification process prove to be biologically relevant.

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Cited By

Sen DPaladhi SFrnda JChatterjee SBanerjee SNedoma J(2022)Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression DataIEEE Access10.1109/ACCESS.2022.319893710(88340-88353)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3198937
Giugno RPulvirenti ACascione LPigola GFerro A(2013)MIDClass: Microarray Data Classification by Association Rules and Gene Expression IntervalsPLoS ONE10.1371/journal.pone.00698738:8(e69873)Online publication date: 6-Aug-2013
https://doi.org/10.1371/journal.pone.0069873
Antonie LBessonov K(2012)Biologically relevant association rules for classification of microarray dataACM SIGAPP Applied Computing Review10.1145/2188379.218838112:1(12-23)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1145/2188379.2188381

Index Terms

Classifying microarray data with association rules
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Conferences

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

March 2011

1868 pages

ISBN:9781450301138

DOI:10.1145/1982185

Conference Chairs:
William Chu
Tunghai University, TaiChung, Taiwan
,
W. Eric Wong
University of Texas at Dallas, Richardson, Texas
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University, Indianapolis
,
Chih-Cheng Hung
Southern Polytechnic State University, Marietta

Copyright © 2011 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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Association for Computing Machinery

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Published: 21 March 2011

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SAC'11: The 2011 ACM Symposium on Applied Computing

March 21 - 24, 2011

TaiChung, Taiwan

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Cited By

Sen DPaladhi SFrnda JChatterjee SBanerjee SNedoma J(2022)Associative Classifier Coupled With Unsupervised Feature Reduction for Dengue Fever Classification Using Gene Expression DataIEEE Access10.1109/ACCESS.2022.319893710(88340-88353)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3198937
Giugno RPulvirenti ACascione LPigola GFerro A(2013)MIDClass: Microarray Data Classification by Association Rules and Gene Expression IntervalsPLoS ONE10.1371/journal.pone.00698738:8(e69873)Online publication date: 6-Aug-2013
https://doi.org/10.1371/journal.pone.0069873
Antonie LBessonov K(2012)Biologically relevant association rules for classification of microarray dataACM SIGAPP Applied Computing Review10.1145/2188379.218838112:1(12-23)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1145/2188379.2188381

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