Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems

Tian-Yu Ren; Fei-Fei Ye; Long-Hao Yang; Jun Liu; Yanyan Wang

doi:10.1109/iske54062.2021.9755363

Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems

Tian-Yu Ren, Fei-Fei Ye, Long-Hao Yang, Jun Liu, Yanyan Wang

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

Abstract

Extended belief rule-based system (EBRBS) as an integrated data and knowledge driven rule-based system has attracted much attention in the last few years and has been widely used in classification problems. Data inconsistency and data incompleteness are two common issues and result in the decrease of the accuracy of data-driven model including EBRBS. Although a dynamic rule activation (DRA) method was proposed to solve these two issues by selecting the most consistent rules and has shown its capability in improving the performance of EBRBS, there still exists some drawbacks in its efficiency and rationality. Hence, a new DRA method based on activation factor (called AFDRA) is proposed for EBRBS to better handle the data inconsistency and data incompleteness issues. Case studies show that the AFDRA method not only has a great improvement in the term of efficiency over the DRA method, but also achieves better performance for EBRBS in classification problems.

Original language	English
Title of host publication	2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
Publisher	IEEE
Pages	82-86
Number of pages	5
ISBN (Electronic)	978-1-6654-0553-9, 978-1-6654-0552-2
ISBN (Print)	978-1-6654-0554-6
DOIs	https://doi.org/10.1109/iske54062.2021.9755363
Publication status	Published (in print/issue) - 18 Apr 2022
Event	16th International Conference on Intelligent Systems and Knowledge Engineering - Chengdu, China Duration: 26 Nov 2021 → 28 Nov 2021

Publication series

Name	2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

Conference

Conference	16th International Conference on Intelligent Systems and Knowledge Engineering
Abbreviated title	ISKE
Country/Territory	China
City	Chengdu
Period	26/11/21 → 28/11/21

Bibliographical note

Funding Information:
This research was supported by the National Natural Science Foundation of China (Nos. 72001043 and 71872047), the Natural Science Foundation of Fujian Province of China (No. 2020J05122), the Humanities and Social Science Foundation of the Ministry of Education of China (Nos. 20YJC630188 and 19YJC630022), the Social Science Planning Fund Project of Fujian Province of China (No. FJ2019C032).

Publisher Copyright:
© 2021 IEEE.

Keywords

activation factor
dynamic rule activation
extended belief rule based system
incompleteness
inconsistency

UN SDGs

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

Access to Document

10.1109/iske54062.2021.9755363

Cite this

Ren, T-Y., Ye, F-F., Yang, L-H., Liu, J., & Wang, Y. (2022). Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems. In 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (pp. 82-86). (2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021). IEEE. https://doi.org/10.1109/iske54062.2021.9755363

@inproceedings{8842fa5116c74d9096a9516000d3441f,

title = "Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems",

abstract = "Extended belief rule-based system (EBRBS) as an integrated data and knowledge driven rule-based system has attracted much attention in the last few years and has been widely used in classification problems. Data inconsistency and data incompleteness are two common issues and result in the decrease of the accuracy of data-driven model including EBRBS. Although a dynamic rule activation (DRA) method was proposed to solve these two issues by selecting the most consistent rules and has shown its capability in improving the performance of EBRBS, there still exists some drawbacks in its efficiency and rationality. Hence, a new DRA method based on activation factor (called AFDRA) is proposed for EBRBS to better handle the data inconsistency and data incompleteness issues. Case studies show that the AFDRA method not only has a great improvement in the term of efficiency over the DRA method, but also achieves better performance for EBRBS in classification problems.",

keywords = "activation factor, dynamic rule activation, extended belief rule based system, incompleteness, inconsistency",

author = "Tian-Yu Ren and Fei-Fei Ye and Long-Hao Yang and Jun Liu and Yanyan Wang",

note = "Funding Information: This research was supported by the National Natural Science Foundation of China (Nos. 72001043 and 71872047), the Natural Science Foundation of Fujian Province of China (No. 2020J05122), the Humanities and Social Science Foundation of the Ministry of Education of China (Nos. 20YJC630188 and 19YJC630022), the Social Science Planning Fund Project of Fujian Province of China (No. FJ2019C032). Publisher Copyright: {\textcopyright} 2021 IEEE.; 16th International Conference on Intelligent Systems and Knowledge Engineering, ISKE ; Conference date: 26-11-2021 Through 28-11-2021",

year = "2022",

month = apr,

day = "18",

doi = "10.1109/iske54062.2021.9755363",

language = "English",

isbn = "978-1-6654-0554-6",

series = "2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021",

publisher = "IEEE",

pages = "82--86",

booktitle = "2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)",

address = "United States",

}

Ren, T-Y, Ye, F-F, Yang, L-H, Liu, J & Wang, Y 2022, Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems. in 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). 2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021, IEEE, pp. 82-86, 16th International Conference on Intelligent Systems and Knowledge Engineering, Chengdu, China, 26/11/21. https://doi.org/10.1109/iske54062.2021.9755363

Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems. / Ren, Tian-Yu; Ye, Fei-Fei; Yang, Long-Hao et al.
2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2022. p. 82-86 (2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021).

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

TY - GEN

T1 - Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems

AU - Ren, Tian-Yu

AU - Ye, Fei-Fei

AU - Yang, Long-Hao

AU - Liu, Jun

AU - Wang, Yanyan

N1 - Funding Information: This research was supported by the National Natural Science Foundation of China (Nos. 72001043 and 71872047), the Natural Science Foundation of Fujian Province of China (No. 2020J05122), the Humanities and Social Science Foundation of the Ministry of Education of China (Nos. 20YJC630188 and 19YJC630022), the Social Science Planning Fund Project of Fujian Province of China (No. FJ2019C032). Publisher Copyright: © 2021 IEEE.

PY - 2022/4/18

Y1 - 2022/4/18

N2 - Extended belief rule-based system (EBRBS) as an integrated data and knowledge driven rule-based system has attracted much attention in the last few years and has been widely used in classification problems. Data inconsistency and data incompleteness are two common issues and result in the decrease of the accuracy of data-driven model including EBRBS. Although a dynamic rule activation (DRA) method was proposed to solve these two issues by selecting the most consistent rules and has shown its capability in improving the performance of EBRBS, there still exists some drawbacks in its efficiency and rationality. Hence, a new DRA method based on activation factor (called AFDRA) is proposed for EBRBS to better handle the data inconsistency and data incompleteness issues. Case studies show that the AFDRA method not only has a great improvement in the term of efficiency over the DRA method, but also achieves better performance for EBRBS in classification problems.

AB - Extended belief rule-based system (EBRBS) as an integrated data and knowledge driven rule-based system has attracted much attention in the last few years and has been widely used in classification problems. Data inconsistency and data incompleteness are two common issues and result in the decrease of the accuracy of data-driven model including EBRBS. Although a dynamic rule activation (DRA) method was proposed to solve these two issues by selecting the most consistent rules and has shown its capability in improving the performance of EBRBS, there still exists some drawbacks in its efficiency and rationality. Hence, a new DRA method based on activation factor (called AFDRA) is proposed for EBRBS to better handle the data inconsistency and data incompleteness issues. Case studies show that the AFDRA method not only has a great improvement in the term of efficiency over the DRA method, but also achieves better performance for EBRBS in classification problems.

KW - activation factor

KW - dynamic rule activation

KW - extended belief rule based system

KW - incompleteness

KW - inconsistency

UR - http://www.scopus.com/inward/record.url?scp=85129431465&partnerID=8YFLogxK

U2 - 10.1109/iske54062.2021.9755363

DO - 10.1109/iske54062.2021.9755363

M3 - Conference contribution

SN - 978-1-6654-0554-6

T3 - 2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

SP - 82

EP - 86

BT - 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

PB - IEEE

T2 - 16th International Conference on Intelligent Systems and Knowledge Engineering

Y2 - 26 November 2021 through 28 November 2021

ER -

Ren T-Y, Ye F-F, Yang L-H, Liu J, Wang Y. Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems. In 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE. 2022. p. 82-86. (2021 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021). doi: 10.1109/iske54062.2021.9755363

Dynamic Rule Activation Method Based on Activation Factor for Extended Belief Rule-based Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this