Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA

Elnaz Pashaei; Mustafa Ozen; Nizamettin Aydin

doi:10.1109/BHI.2016.7455896

Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA

Elnaz Pashaei, Mustafa Ozen, Nizamettin Aydin

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

32 Citations (Scopus)

Abstract

In this paper, a novel approach based on Binary Black Hole Algorithm (BBHA) and Random Forest Ranking (RFR) is proposed for gene selection and classification of microarray data. In this approach, RFR and BBHA are used to perform gene selection to remove irrelevant and redundant genes. Because of its ability in reducing noise, bias and variance errors Bagging with 10-fold cross validation is selected as a classifier. The result of RFR-BBHA-Bagging is compared to seven benchmark classification methods. Experimental results show that our proposed method by selecting the least number of informative genes can increase prediction accuracy of Bagging and outperforms the other classification methods.

Original language	English
Title of host publication	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	308-311
Number of pages	4
ISBN (Electronic)	9781509024551
DOIs	https://doi.org/10.1109/BHI.2016.7455896
Publication status	Published - 18 Apr 2016
Externally published	Yes
Event	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States Duration: 24 Feb 2016 → 27 Feb 2016

Publication series

Name	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016

Conference

Conference	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Country/Territory	United States
City	Las Vegas
Period	24/02/16 → 27/02/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

bagging
black hole algorithm
Gene selection
random forest ranking

Access to Document

10.1109/BHI.2016.7455896

Cite this

Pashaei, E., Ozen, M., & Aydin, N. (2016). Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA. In 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 (pp. 308-311). Article 7455896 (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2016.7455896

Pashaei, Elnaz ; Ozen, Mustafa ; Aydin, Nizamettin. / Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA. 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 308-311 (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016).

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title = "Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA",

abstract = "In this paper, a novel approach based on Binary Black Hole Algorithm (BBHA) and Random Forest Ranking (RFR) is proposed for gene selection and classification of microarray data. In this approach, RFR and BBHA are used to perform gene selection to remove irrelevant and redundant genes. Because of its ability in reducing noise, bias and variance errors Bagging with 10-fold cross validation is selected as a classifier. The result of RFR-BBHA-Bagging is compared to seven benchmark classification methods. Experimental results show that our proposed method by selecting the least number of informative genes can increase prediction accuracy of Bagging and outperforms the other classification methods.",

keywords = "bagging, black hole algorithm, Gene selection, random forest ranking",

author = "Elnaz Pashaei and Mustafa Ozen and Nizamettin Aydin",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 ; Conference date: 24-02-2016 Through 27-02-2016",

year = "2016",

month = apr,

day = "18",

doi = "10.1109/BHI.2016.7455896",

language = "English",

series = "3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Pashaei, E, Ozen, M & Aydin, N 2016, Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA. in 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016., 7455896, 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 308-311, 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, Las Vegas, United States, 24/02/16. https://doi.org/10.1109/BHI.2016.7455896

Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA. / Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin.
3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 308-311 7455896 (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016).

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

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N2 - In this paper, a novel approach based on Binary Black Hole Algorithm (BBHA) and Random Forest Ranking (RFR) is proposed for gene selection and classification of microarray data. In this approach, RFR and BBHA are used to perform gene selection to remove irrelevant and redundant genes. Because of its ability in reducing noise, bias and variance errors Bagging with 10-fold cross validation is selected as a classifier. The result of RFR-BBHA-Bagging is compared to seven benchmark classification methods. Experimental results show that our proposed method by selecting the least number of informative genes can increase prediction accuracy of Bagging and outperforms the other classification methods.

AB - In this paper, a novel approach based on Binary Black Hole Algorithm (BBHA) and Random Forest Ranking (RFR) is proposed for gene selection and classification of microarray data. In this approach, RFR and BBHA are used to perform gene selection to remove irrelevant and redundant genes. Because of its ability in reducing noise, bias and variance errors Bagging with 10-fold cross validation is selected as a classifier. The result of RFR-BBHA-Bagging is compared to seven benchmark classification methods. Experimental results show that our proposed method by selecting the least number of informative genes can increase prediction accuracy of Bagging and outperforms the other classification methods.

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Pashaei E, Ozen M, Aydin N. Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA. In 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 308-311. 7455896. (3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016). doi: 10.1109/BHI.2016.7455896

Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA

Abstract

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Keywords

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