An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach

Chao Ma; Jian Guan; Wenyong Zhao; Chaolun Wang

doi:10.1007/978-3-319-94307-7_7

An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach

Chao Ma
, Jian Guan
, Wenyong Zhao
, Chaolun Wang^*

^*Corresponding author for this work

Shenzhen Institute of Information Technology
Harbin Institute of Technology Shenzhen
DFH Satellite Co., Ltd.

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

9 Scopus citations

Abstract

In this paper, we present a novel hybrid diagnosis system named LFDA-EKELM, which integrates local fisher discriminant analysis (LFDA) and kernelized extreme learning machine method for thyroid disease diagnosis. The proposed method comprises of three stages. Focusing on dimension reduction, the first stage employs LFDA as a feature extraction tool to construct more discriminative subspace for classification, the system switches from feature extraction to model construction. And then, the obtained feature subsets are fed into designed kernelized ELM (KELM) classifier to train an optimal predictor model whose parameters are adaptively specified by improving artificial bee colony (IABC) approach. Here, the proposed IABC method introduces an improved solution search equation to enhance the exploitation of searching for solutions, and provides a new framework to make the global converge rapidly. Finally, the enhanced-KELM (EKELM) model is applied to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed system is evaluated on the thyroid disease dataset in terms of classification accuracy. Experimental results demonstrate that LFDA-EKELM outperforms the baseline methods.

Original language	English
Title of host publication	Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings
Editors	Zhi-Hong Mao, Jing Xiao, Toyotaro Suzumura, Liang-Jie Zhang
Publisher	Springer Verlag
Pages	86-101
Number of pages	16
ISBN (Print)	9783319943060
DOIs	https://doi.org/10.1007/978-3-319-94307-7_7
State	Published - 2018
Externally published	Yes
Event	2nd International Conference on Cognitive Computing, ICCC 2018 Held as Part of the Services Conference Federation, SCF 2018 - Seattle, United States Duration: 25 Jun 2018 → 30 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10971 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Cognitive Computing, ICCC 2018 Held as Part of the Services Conference Federation, SCF 2018
Country/Territory	United States
City	Seattle
Period	25/06/18 → 30/06/18

Keywords

Artificial bee colony algorithm
Feature extraction
Kernelized extreme learning machine
Medical diagnosis

Access to Document

10.1007/978-3-319-94307-7_7

Cite this

Ma, C., Guan, J., Zhao, W., & Wang, C. (2018). An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach. In Z.-H. Mao, J. Xiao, T. Suzumura, & L.-J. Zhang (Eds.), Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings (pp. 86-101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10971 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-94307-7_7

Ma, Chao ; Guan, Jian ; Zhao, Wenyong et al. / An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach. Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. editor / Zhi-Hong Mao ; Jing Xiao ; Toyotaro Suzumura ; Liang-Jie Zhang. Springer Verlag, 2018. pp. 86-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{04b292bf4c8642fb99a181099b852a94,

title = "An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach",

abstract = "In this paper, we present a novel hybrid diagnosis system named LFDA-EKELM, which integrates local fisher discriminant analysis (LFDA) and kernelized extreme learning machine method for thyroid disease diagnosis. The proposed method comprises of three stages. Focusing on dimension reduction, the first stage employs LFDA as a feature extraction tool to construct more discriminative subspace for classification, the system switches from feature extraction to model construction. And then, the obtained feature subsets are fed into designed kernelized ELM (KELM) classifier to train an optimal predictor model whose parameters are adaptively specified by improving artificial bee colony (IABC) approach. Here, the proposed IABC method introduces an improved solution search equation to enhance the exploitation of searching for solutions, and provides a new framework to make the global converge rapidly. Finally, the enhanced-KELM (EKELM) model is applied to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed system is evaluated on the thyroid disease dataset in terms of classification accuracy. Experimental results demonstrate that LFDA-EKELM outperforms the baseline methods.",

keywords = "Artificial bee colony algorithm, Feature extraction, Kernelized extreme learning machine, Medical diagnosis",

author = "Chao Ma and Jian Guan and Wenyong Zhao and Chaolun Wang",

note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 2nd International Conference on Cognitive Computing, ICCC 2018 Held as Part of the Services Conference Federation, SCF 2018 ; Conference date: 25-06-2018 Through 30-06-2018",

year = "2018",

doi = "10.1007/978-3-319-94307-7\_7",

language = "英语",

isbn = "9783319943060",

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

publisher = "Springer Verlag",

pages = "86--101",

editor = "Zhi-Hong Mao and Jing Xiao and Toyotaro Suzumura and Liang-Jie Zhang",

booktitle = "Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings",

address = "德国",

}

Ma, C, Guan, J, Zhao, W & Wang, C 2018, An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach. in Z-H Mao, J Xiao, T Suzumura & L-J Zhang (eds), Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10971 LNCS, Springer Verlag, pp. 86-101, 2nd International Conference on Cognitive Computing, ICCC 2018 Held as Part of the Services Conference Federation, SCF 2018, Seattle, United States, 25/06/18. https://doi.org/10.1007/978-3-319-94307-7_7

An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach. / Ma, Chao; Guan, Jian; Zhao, Wenyong et al.
Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. ed. / Zhi-Hong Mao; Jing Xiao; Toyotaro Suzumura; Liang-Jie Zhang. Springer Verlag, 2018. p. 86-101 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10971 LNCS).

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

TY - GEN

T1 - An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach

AU - Ma, Chao

AU - Guan, Jian

AU - Zhao, Wenyong

AU - Wang, Chaolun

PY - 2018

Y1 - 2018

N2 - In this paper, we present a novel hybrid diagnosis system named LFDA-EKELM, which integrates local fisher discriminant analysis (LFDA) and kernelized extreme learning machine method for thyroid disease diagnosis. The proposed method comprises of three stages. Focusing on dimension reduction, the first stage employs LFDA as a feature extraction tool to construct more discriminative subspace for classification, the system switches from feature extraction to model construction. And then, the obtained feature subsets are fed into designed kernelized ELM (KELM) classifier to train an optimal predictor model whose parameters are adaptively specified by improving artificial bee colony (IABC) approach. Here, the proposed IABC method introduces an improved solution search equation to enhance the exploitation of searching for solutions, and provides a new framework to make the global converge rapidly. Finally, the enhanced-KELM (EKELM) model is applied to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed system is evaluated on the thyroid disease dataset in terms of classification accuracy. Experimental results demonstrate that LFDA-EKELM outperforms the baseline methods.

AB - In this paper, we present a novel hybrid diagnosis system named LFDA-EKELM, which integrates local fisher discriminant analysis (LFDA) and kernelized extreme learning machine method for thyroid disease diagnosis. The proposed method comprises of three stages. Focusing on dimension reduction, the first stage employs LFDA as a feature extraction tool to construct more discriminative subspace for classification, the system switches from feature extraction to model construction. And then, the obtained feature subsets are fed into designed kernelized ELM (KELM) classifier to train an optimal predictor model whose parameters are adaptively specified by improving artificial bee colony (IABC) approach. Here, the proposed IABC method introduces an improved solution search equation to enhance the exploitation of searching for solutions, and provides a new framework to make the global converge rapidly. Finally, the enhanced-KELM (EKELM) model is applied to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed system is evaluated on the thyroid disease dataset in terms of classification accuracy. Experimental results demonstrate that LFDA-EKELM outperforms the baseline methods.

KW - Artificial bee colony algorithm

KW - Feature extraction

KW - Kernelized extreme learning machine

KW - Medical diagnosis

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U2 - 10.1007/978-3-319-94307-7_7

DO - 10.1007/978-3-319-94307-7_7

M3 - 会议稿件

AN - SCOPUS:85049472671

SN - 9783319943060

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

SP - 86

EP - 101

BT - Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings

A2 - Mao, Zhi-Hong

A2 - Xiao, Jing

A2 - Suzumura, Toyotaro

A2 - Zhang, Liang-Jie

PB - Springer Verlag

T2 - 2nd International Conference on Cognitive Computing, ICCC 2018 Held as Part of the Services Conference Federation, SCF 2018

Y2 - 25 June 2018 through 30 June 2018

ER -

Ma C, Guan J, Zhao W, Wang C. An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach. In Mao ZH, Xiao J, Suzumura T, Zhang LJ, editors, Cognitive Computing – ICCC 2018 - 2nd International Conference, Held as Part of the Services Conference Federation, SCF 2018, Proceedings. Springer Verlag. 2018. p. 86-101. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-94307-7_7

An Efficient Diagnosis System for Thyroid Disease Based on Enhanced Kernelized Extreme Learning Machine Approach

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Keywords

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