Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening

Choujun Zhan; Benjamin Yee Shing Li; Quansi Wen; Gao Ying; Tianyong Hao

doi:10.1109/BIBM47256.2019.8982993

Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening

Choujun Zhan
, Benjamin Yee Shing Li
, Quansi Wen^*
, Gao Ying
, Tianyong Hao

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Imbalanced dataset is a common issue in many applications. The one-class Support Vector Machine (SVM) is found to be an effective algorithm to construct classification models over the underlying imbalanced dataset. In some cases, feature extraction is hard and one would prefer using pre-defined kernels to train the model. In traditional practice, a valid kernel has to satisfy the Mercer's condition, which may restrict the design of kernel functions or matrices. In this paper, an indefinite kernel extension is applied to the one-class SVM model in order to relieve such limitation. To illustrate its performance, the algorithm is applied to perform virtual screening of drugs.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Hu
Publisher	IEEE
Pages	1149-1155
ISBN (Print)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8982993
Publication status	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019 https://ieeebibm.org/BIBM2019/

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Name	Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)
Abbreviated title	IEEE BIBM 2019
Place	United States
City	San Diego
Period	18/11/19 → 21/11/19
Internet address	https://ieeebibm.org/BIBM2019/

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10.1109/BIBM47256.2019.8982993

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Zhan, C., Li, B. Y. S., Wen, Q., Ying, G., & Hao, T. (2019). Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening. In I. Yoo, J. Bi, & X. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine (pp. 1149-1155). Article 8982993 (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM). IEEE. https://doi.org/10.1109/BIBM47256.2019.8982993

Zhan, Choujun ; Li, Benjamin Yee Shing ; Wen, Quansi et al. / Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Hu. IEEE, 2019. pp. 1149-1155 (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM).

@inproceedings{00e6a315e58f484d95c7d5be2c2648dd,

title = "Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening",

abstract = "Imbalanced dataset is a common issue in many applications. The one-class Support Vector Machine (SVM) is found to be an effective algorithm to construct classification models over the underlying imbalanced dataset. In some cases, feature extraction is hard and one would prefer using pre-defined kernels to train the model. In traditional practice, a valid kernel has to satisfy the Mercer's condition, which may restrict the design of kernel functions or matrices. In this paper, an indefinite kernel extension is applied to the one-class SVM model in order to relieve such limitation. To illustrate its performance, the algorithm is applied to perform virtual screening of drugs.",

author = "Choujun Zhan and Li, \{Benjamin Yee Shing\} and Quansi Wen and Gao Ying and Tianyong Hao",

year = "2019",

month = nov,

doi = "10.1109/BIBM47256.2019.8982993",

language = "English",

isbn = "9781728118673",

series = "Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM",

publisher = "IEEE",

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booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine",

address = "United States",

note = "2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019), IEEE BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

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}

Zhan, C, Li, BYS, Wen, Q, Ying, G & Hao, T 2019, Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening. in I Yoo, J Bi & X Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine., 8982993, Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM, IEEE, pp. 1149-1155, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019), San Diego, California, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8982993

Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening. / Zhan, Choujun; Li, Benjamin Yee Shing; Wen, Quansi et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Hu. IEEE, 2019. p. 1149-1155 8982993 (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening

AU - Zhan, Choujun

AU - Li, Benjamin Yee Shing

AU - Wen, Quansi

AU - Ying, Gao

AU - Hao, Tianyong

PY - 2019/11

Y1 - 2019/11

N2 - Imbalanced dataset is a common issue in many applications. The one-class Support Vector Machine (SVM) is found to be an effective algorithm to construct classification models over the underlying imbalanced dataset. In some cases, feature extraction is hard and one would prefer using pre-defined kernels to train the model. In traditional practice, a valid kernel has to satisfy the Mercer's condition, which may restrict the design of kernel functions or matrices. In this paper, an indefinite kernel extension is applied to the one-class SVM model in order to relieve such limitation. To illustrate its performance, the algorithm is applied to perform virtual screening of drugs.

AB - Imbalanced dataset is a common issue in many applications. The one-class Support Vector Machine (SVM) is found to be an effective algorithm to construct classification models over the underlying imbalanced dataset. In some cases, feature extraction is hard and one would prefer using pre-defined kernels to train the model. In traditional practice, a valid kernel has to satisfy the Mercer's condition, which may restrict the design of kernel functions or matrices. In this paper, an indefinite kernel extension is applied to the one-class SVM model in order to relieve such limitation. To illustrate its performance, the algorithm is applied to perform virtual screening of drugs.

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781728118673

T3 - Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM

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BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine

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PB - IEEE

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ER -

Zhan C, Li BYS, Wen Q, Ying G, Hao T. Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening. In Yoo I, Bi J, Hu X, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine. IEEE. 2019. p. 1149-1155. 8982993. (Proceedings - IEEE International Conference on Bioinformatics and Biomedicine, BIBM). doi: 10.1109/BIBM47256.2019.8982993

Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening

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