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

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

32 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	308-311
Sayfa sayısı	4
ISBN (Elektronik)	9781509024551
DOI'lar	https://doi.org/10.1109/BHI.2016.7455896
Yayın durumu	Yayınlandı - 18 Nis 2016
Harici olarak yayınlandı	Evet
Etkinlik	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States Süre: 24 Şub 2016 → 27 Şub 2016

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Ülke/Bölge	United States
Şehir	Las Vegas
Periyot	24/02/16 → 27/02/16

Bibliyografik not

Publisher Copyright:
© 2016 IEEE.

Belgeye Erişim

10.1109/BHI.2016.7455896

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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). Makale 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).

@inproceedings{7f4676061d1c48c7ba73953b8b9a7d8e,

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.",

pages = "308--311",

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

address = "United States",

}

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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA

AU - Pashaei, Elnaz

AU - Ozen, Mustafa

AU - Aydin, Nizamettin

PY - 2016/4/18

Y1 - 2016/4/18

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.

KW - bagging

KW - black hole algorithm

KW - Gene selection

KW - random forest ranking

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U2 - 10.1109/BHI.2016.7455896

DO - 10.1109/BHI.2016.7455896

M3 - Conference contribution

AN - SCOPUS:84968655950

T3 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016

SP - 308

EP - 311

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016

Y2 - 24 February 2016 through 27 February 2016

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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

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