Research on Feature Selection Method Based on Bayesian Network and Importance Measures

Jingwei Zhang; Chen Chen; Yuhan Wu; Shubin Si; Zhimin Geng; Zhiqiang Cai

doi:10.1109/ICRMS55680.2022.9944576

Research on Feature Selection Method Based on Bayesian Network and Importance Measures

Jingwei Zhang
, Chen Chen
, Yuhan Wu
, Shubin Si
, Zhimin Geng
, Zhiqiang Cai

School of Mechanical Engineering

Northwestern Polytechnical University Xian
The First Affiliated Hospital of Xi’an Jiaotong University

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

1 Scopus citations

Abstract

With the wide application of machine learning algorithms in various fields, feature selection becomes more and more important as a data preprocessing method which can not only solve the problem of dimension disaster, but also improve the generalization ability of algorithms. Based on this, the main work of this paper is as follows. Firstly, the importance measures and Bayesian network were combined to solve the problem that Bayesian network could not rank the importance of features. At the same time, a recursive feature elimination algorithm based on importance degree theory is proposed with importance degree as the screening index. Finally, the prognostic model of gallbladder cancer was established, which shows that the proposed algorithm has good performance.

Original language	English
Title of host publication	13th International Conference on Reliability, Maintainability, and Safety
Subtitle of host publication	Reliability and Safety of Intelligent Systems, ICRMS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	18-22
Number of pages	5
ISBN (Electronic)	9781665486903
DOIs	https://doi.org/10.1109/ICRMS55680.2022.9944576
State	Published - 2022
Event	13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022 - Hong Kong, China Duration: 21 Aug 2022 → 24 Aug 2022

Publication series

Name	13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022

Conference

Conference	13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022
Country/Territory	China
City	Hong Kong
Period	21/08/22 → 24/08/22

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Bayesian networks
feature selection
Importance measures

Access to Document

10.1109/ICRMS55680.2022.9944576

Cite this

Zhang, J., Chen, C., Wu, Y., Si, S., Geng, Z., & Cai, Z. (2022). Research on Feature Selection Method Based on Bayesian Network and Importance Measures. In 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022 (pp. 18-22). (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRMS55680.2022.9944576

Zhang, Jingwei ; Chen, Chen ; Wu, Yuhan et al. / Research on Feature Selection Method Based on Bayesian Network and Importance Measures. 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 18-22 (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022).

@inproceedings{5909b8247a254e87ae31ff89713d2728,

title = "Research on Feature Selection Method Based on Bayesian Network and Importance Measures",

abstract = "With the wide application of machine learning algorithms in various fields, feature selection becomes more and more important as a data preprocessing method which can not only solve the problem of dimension disaster, but also improve the generalization ability of algorithms. Based on this, the main work of this paper is as follows. Firstly, the importance measures and Bayesian network were combined to solve the problem that Bayesian network could not rank the importance of features. At the same time, a recursive feature elimination algorithm based on importance degree theory is proposed with importance degree as the screening index. Finally, the prognostic model of gallbladder cancer was established, which shows that the proposed algorithm has good performance.",

keywords = "Bayesian networks, feature selection, Importance measures",

author = "Jingwei Zhang and Chen Chen and Yuhan Wu and Shubin Si and Zhimin Geng and Zhiqiang Cai",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022 ; Conference date: 21-08-2022 Through 24-08-2022",

year = "2022",

doi = "10.1109/ICRMS55680.2022.9944576",

language = "英语",

series = "13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022",

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

pages = "18--22",

booktitle = "13th International Conference on Reliability, Maintainability, and Safety",

}

Zhang, J, Chen, C, Wu, Y, Si, S, Geng, Z & Cai, Z 2022, Research on Feature Selection Method Based on Bayesian Network and Importance Measures. in 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022. 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 18-22, 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022, Hong Kong, China, 21/08/22. https://doi.org/10.1109/ICRMS55680.2022.9944576

Research on Feature Selection Method Based on Bayesian Network and Importance Measures. / Zhang, Jingwei; Chen, Chen; Wu, Yuhan et al.
13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 18-22 (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022).

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

TY - GEN

T1 - Research on Feature Selection Method Based on Bayesian Network and Importance Measures

AU - Zhang, Jingwei

AU - Chen, Chen

AU - Wu, Yuhan

AU - Si, Shubin

AU - Geng, Zhimin

AU - Cai, Zhiqiang

PY - 2022

Y1 - 2022

N2 - With the wide application of machine learning algorithms in various fields, feature selection becomes more and more important as a data preprocessing method which can not only solve the problem of dimension disaster, but also improve the generalization ability of algorithms. Based on this, the main work of this paper is as follows. Firstly, the importance measures and Bayesian network were combined to solve the problem that Bayesian network could not rank the importance of features. At the same time, a recursive feature elimination algorithm based on importance degree theory is proposed with importance degree as the screening index. Finally, the prognostic model of gallbladder cancer was established, which shows that the proposed algorithm has good performance.

AB - With the wide application of machine learning algorithms in various fields, feature selection becomes more and more important as a data preprocessing method which can not only solve the problem of dimension disaster, but also improve the generalization ability of algorithms. Based on this, the main work of this paper is as follows. Firstly, the importance measures and Bayesian network were combined to solve the problem that Bayesian network could not rank the importance of features. At the same time, a recursive feature elimination algorithm based on importance degree theory is proposed with importance degree as the screening index. Finally, the prognostic model of gallbladder cancer was established, which shows that the proposed algorithm has good performance.

KW - Bayesian networks

KW - feature selection

KW - Importance measures

UR - https://www.scopus.com/pages/publications/85143065068

U2 - 10.1109/ICRMS55680.2022.9944576

DO - 10.1109/ICRMS55680.2022.9944576

M3 - 会议稿件

AN - SCOPUS:85143065068

T3 - 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022

SP - 18

EP - 22

BT - 13th International Conference on Reliability, Maintainability, and Safety

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

Y2 - 21 August 2022 through 24 August 2022

ER -

Zhang J, Chen C, Wu Y, Si S, Geng Z, Cai Z. Research on Feature Selection Method Based on Bayesian Network and Importance Measures. In 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 18-22. (13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022). doi: 10.1109/ICRMS55680.2022.9944576

Research on Feature Selection Method Based on Bayesian Network and Importance Measures

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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