Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification

Zhen Xin Niu; Liang Rui Ren; Rong Zhu; Xiang Zhen Kong; Ying Lian Gao; Jin Xing Liu

doi:10.1007/978-3-030-84529-2_45

Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification

Zhen Xin Niu
, Liang Rui Ren
, Rong Zhu
, Xiang Zhen Kong
, Ying Lian Gao
, Jin Xing Liu

Qufu Normal University

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

Abstract

In recent years, Extreme Learning Machine (ELM) has attracted extensive attention in various research fields. To improve the performance of ELM, we propose an Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss (DKRSLELM) method in this paper. The Kernel Risk-Sensitive Loss (KRSL) is integrated into the objective function of ELM. This is because KRSL not only can effectively reduce the influence of noise and outliers, but also can eliminate the redundant neurons of ELM. In the experiment, we conduct a classification experiment on cancer integration data-sets. The experimental results indicate that our method can effectively improve the classification performance of ELM.

Original language	English
Title of host publication	Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings
Editors	De-Shuang Huang, Kang-Hyun Jo, Jianqiang Li, Valeriya Gribova, Abir Hussain
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	532-539
Number of pages	8
ISBN (Print)	9783030845285
DOIs	https://doi.org/10.1007/978-3-030-84529-2_45
State	Published - 2021
Externally published	Yes
Event	17th International Conference on Intelligent Computing, ICIC 2021 - Shenzhen, China Duration: 12 Aug 2021 → 15 Aug 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12837 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Intelligent Computing, ICIC 2021
Country/Territory	China
City	Shenzhen
Period	12/08/21 → 15/08/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Extreme Learning Machine
Kernel Risk-Sensitive Loss
Robustness
Sparsity
Supervised learning

Access to Document

10.1007/978-3-030-84529-2_45

Cite this

Niu, Z. X., Ren, L. R., Zhu, R., Kong, X. Z., Gao, Y. L., & Liu, J. X. (2021). Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification. In D.-S. Huang, K.-H. Jo, J. Li, V. Gribova, & A. Hussain (Eds.), Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings (pp. 532-539). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12837 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-84529-2_45

Niu, Zhen Xin ; Ren, Liang Rui ; Zhu, Rong et al. / Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification. Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings. editor / De-Shuang Huang ; Kang-Hyun Jo ; Jianqiang Li ; Valeriya Gribova ; Abir Hussain. Springer Science and Business Media Deutschland GmbH, 2021. pp. 532-539 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{60749b0f34c942ca83c1fd292f0c694f,

title = "Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification",

abstract = "In recent years, Extreme Learning Machine (ELM) has attracted extensive attention in various research fields. To improve the performance of ELM, we propose an Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss (DKRSLELM) method in this paper. The Kernel Risk-Sensitive Loss (KRSL) is integrated into the objective function of ELM. This is because KRSL not only can effectively reduce the influence of noise and outliers, but also can eliminate the redundant neurons of ELM. In the experiment, we conduct a classification experiment on cancer integration data-sets. The experimental results indicate that our method can effectively improve the classification performance of ELM.",

keywords = "Extreme Learning Machine, Kernel Risk-Sensitive Loss, Robustness, Sparsity, Supervised learning",

author = "Niu, \{Zhen Xin\} and Ren, \{Liang Rui\} and Rong Zhu and Kong, \{Xiang Zhen\} and Gao, \{Ying Lian\} and Liu, \{Jin Xing\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 17th International Conference on Intelligent Computing, ICIC 2021 ; Conference date: 12-08-2021 Through 15-08-2021",

year = "2021",

doi = "10.1007/978-3-030-84529-2\_45",

language = "英语",

isbn = "9783030845285",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "532--539",

editor = "De-Shuang Huang and Kang-Hyun Jo and Jianqiang Li and Valeriya Gribova and Abir Hussain",

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}

Niu, ZX, Ren, LR, Zhu, R, Kong, XZ, Gao, YL & Liu, JX 2021, Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification. in D-S Huang, K-H Jo, J Li, V Gribova & A Hussain (eds), Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12837 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 532-539, 17th International Conference on Intelligent Computing, ICIC 2021, Shenzhen, China, 12/08/21. https://doi.org/10.1007/978-3-030-84529-2_45

Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification. / Niu, Zhen Xin; Ren, Liang Rui; Zhu, Rong et al.
Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings. ed. / De-Shuang Huang; Kang-Hyun Jo; Jianqiang Li; Valeriya Gribova; Abir Hussain. Springer Science and Business Media Deutschland GmbH, 2021. p. 532-539 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12837 LNCS).

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

TY - GEN

T1 - Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification

AU - Niu, Zhen Xin

AU - Ren, Liang Rui

AU - Zhu, Rong

AU - Kong, Xiang Zhen

AU - Gao, Ying Lian

AU - Liu, Jin Xing

PY - 2021

Y1 - 2021

N2 - In recent years, Extreme Learning Machine (ELM) has attracted extensive attention in various research fields. To improve the performance of ELM, we propose an Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss (DKRSLELM) method in this paper. The Kernel Risk-Sensitive Loss (KRSL) is integrated into the objective function of ELM. This is because KRSL not only can effectively reduce the influence of noise and outliers, but also can eliminate the redundant neurons of ELM. In the experiment, we conduct a classification experiment on cancer integration data-sets. The experimental results indicate that our method can effectively improve the classification performance of ELM.

AB - In recent years, Extreme Learning Machine (ELM) has attracted extensive attention in various research fields. To improve the performance of ELM, we propose an Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss (DKRSLELM) method in this paper. The Kernel Risk-Sensitive Loss (KRSL) is integrated into the objective function of ELM. This is because KRSL not only can effectively reduce the influence of noise and outliers, but also can eliminate the redundant neurons of ELM. In the experiment, we conduct a classification experiment on cancer integration data-sets. The experimental results indicate that our method can effectively improve the classification performance of ELM.

KW - Extreme Learning Machine

KW - Kernel Risk-Sensitive Loss

KW - Robustness

KW - Sparsity

KW - Supervised learning

UR - https://www.scopus.com/pages/publications/85115261607

U2 - 10.1007/978-3-030-84529-2_45

DO - 10.1007/978-3-030-84529-2_45

M3 - 会议稿件

AN - SCOPUS:85115261607

SN - 9783030845285

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 532

EP - 539

BT - Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings

A2 - Huang, De-Shuang

A2 - Jo, Kang-Hyun

A2 - Li, Jianqiang

A2 - Gribova, Valeriya

A2 - Hussain, Abir

PB - Springer Science and Business Media Deutschland GmbH

T2 - 17th International Conference on Intelligent Computing, ICIC 2021

Y2 - 12 August 2021 through 15 August 2021

ER -

Niu ZX, Ren LR, Zhu R, Kong XZ, Gao YL, Liu JX. Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification. In Huang DS, Jo KH, Li J, Gribova V, Hussain A, editors, Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 532-539. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-84529-2_45

Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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