A Micro-Extended Belief Rule-Based System for Big Data Multi-Class Classification Problems

Longhao Yang; J. Liu; Yingming Wang; Luis Martinez

doi:10.1109/TSMC.2018.2872843

A Micro-Extended Belief Rule-Based System for Big Data Multi-Class Classification Problems

Longhao Yang, J. Liu, Yingming Wang, Luis Martinez

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

174 Downloads (Pure)

Abstract

Big data classification problems have drawn great attention from diverse fields, and many classifiers have been developed. Among those classifiers, the extended belief rule-based system (EBRBS) has shown its potential in both big data and multiclass situations, while the time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed first in this paper to decrease the time complexity and computing efficiency of EBRBS for multiclass classification under the assumption of large amount of data, including the strategy to skip rule weight calculation, a simplified evidential reasoning algorithm, and the domain division-based rule reduction method. This turns out to be a micro version of the EBRBS, called Micro-EBRBS. Moreover, one of commonly used cluster computing, named Apache Spark, is then applied to implement the parallel rule generation and inference schemes of the Micro-EBRBS for big data multiclass classification problems. The comparative analyses of experimental studies demonstrate that the Micro-EBRBS not only can obtain a desired accuracy but also has the comparatively better time complexity and computing efficiency than some popular classifiers, especially for multiclass classification problems.

Original language	English
Pages (from-to)	420-440
Number of pages	21
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	51
Issue number	1
Early online date	26 Oct 2018
DOIs	https://doi.org/10.1109/TSMC.2018.2872843
Publication status	Published (in print/issue) - 1 Jan 2021

Bibliographical note

Funding Information:
Manuscript received July 1, 2018; accepted September 11, 2018. Date of publication October 26, 2018; date of current version January 12, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 72001043, Grant 61773123, Grant 71701050, Grant 71501047, and Grant 72001042; in part by the Humanities and Social Science Foundation of the Ministry of Education under Grant 20YJC630188; and in part by the Natural Science Foundation of Fujian Province, China, under Grant 2020J05122. This paper was recommended by Associate Editor L. Wang. (Corresponding author: Ying-Ming Wang.) L.-H. Yang is with the Decision Sciences Institute, Fuzhou University, Fuzhou 350108, China, and also with the Department of Computer Science, University of Jaén, 23008 Jaén, Spain (e-mail: more026@hotmail.com).

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

Apache spark
big data
extended belief rule-based system (EBRBS)
multiclass

Access to Document

10.1109/TSMC.2018.2872843

IEEETSMC_Accepted VersionAuthor Accepted Manuscript, 1.51 MB

Cite this

@article{13509e5064774921bfee4d56816ef7a4,

title = "A Micro-Extended Belief Rule-Based System for Big Data Multi-Class Classification Problems",

abstract = "Big data classification problems have drawn great attention from diverse fields, and many classifiers have been developed. Among those classifiers, the extended belief rule-based system (EBRBS) has shown its potential in both big data and multiclass situations, while the time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed first in this paper to decrease the time complexity and computing efficiency of EBRBS for multiclass classification under the assumption of large amount of data, including the strategy to skip rule weight calculation, a simplified evidential reasoning algorithm, and the domain division-based rule reduction method. This turns out to be a micro version of the EBRBS, called Micro-EBRBS. Moreover, one of commonly used cluster computing, named Apache Spark, is then applied to implement the parallel rule generation and inference schemes of the Micro-EBRBS for big data multiclass classification problems. The comparative analyses of experimental studies demonstrate that the Micro-EBRBS not only can obtain a desired accuracy but also has the comparatively better time complexity and computing efficiency than some popular classifiers, especially for multiclass classification problems.",

keywords = "Apache spark, big data, extended belief rule-based system (EBRBS), multiclass",

author = "Longhao Yang and J. Liu and Yingming Wang and Luis Martinez",

note = "Funding Information: Manuscript received July 1, 2018; accepted September 11, 2018. Date of publication October 26, 2018; date of current version January 12, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 72001043, Grant 61773123, Grant 71701050, Grant 71501047, and Grant 72001042; in part by the Humanities and Social Science Foundation of the Ministry of Education under Grant 20YJC630188; and in part by the Natural Science Foundation of Fujian Province, China, under Grant 2020J05122. This paper was recommended by Associate Editor L. Wang. (Corresponding author: Ying-Ming Wang.) L.-H. Yang is with the Decision Sciences Institute, Fuzhou University, Fuzhou 350108, China, and also with the Department of Computer Science, University of Ja{\'e}n, 23008 Ja{\'e}n, Spain (e-mail: more026@hotmail.com). Publisher Copyright: {\textcopyright} 2013 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = jan,

day = "1",

doi = "10.1109/TSMC.2018.2872843",

language = "English",

volume = "51",

pages = "420--440",

journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems",

issn = "2168-2216",

publisher = "IEEE",

number = "1",

}

TY - JOUR

T1 - A Micro-Extended Belief Rule-Based System for Big Data Multi-Class Classification Problems

AU - Yang, Longhao

AU - Liu, J.

AU - Wang, Yingming

AU - Martinez, Luis

N1 - Funding Information: Manuscript received July 1, 2018; accepted September 11, 2018. Date of publication October 26, 2018; date of current version January 12, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 72001043, Grant 61773123, Grant 71701050, Grant 71501047, and Grant 72001042; in part by the Humanities and Social Science Foundation of the Ministry of Education under Grant 20YJC630188; and in part by the Natural Science Foundation of Fujian Province, China, under Grant 2020J05122. This paper was recommended by Associate Editor L. Wang. (Corresponding author: Ying-Ming Wang.) L.-H. Yang is with the Decision Sciences Institute, Fuzhou University, Fuzhou 350108, China, and also with the Department of Computer Science, University of Jaén, 23008 Jaén, Spain (e-mail: more026@hotmail.com). Publisher Copyright: © 2013 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Big data classification problems have drawn great attention from diverse fields, and many classifiers have been developed. Among those classifiers, the extended belief rule-based system (EBRBS) has shown its potential in both big data and multiclass situations, while the time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed first in this paper to decrease the time complexity and computing efficiency of EBRBS for multiclass classification under the assumption of large amount of data, including the strategy to skip rule weight calculation, a simplified evidential reasoning algorithm, and the domain division-based rule reduction method. This turns out to be a micro version of the EBRBS, called Micro-EBRBS. Moreover, one of commonly used cluster computing, named Apache Spark, is then applied to implement the parallel rule generation and inference schemes of the Micro-EBRBS for big data multiclass classification problems. The comparative analyses of experimental studies demonstrate that the Micro-EBRBS not only can obtain a desired accuracy but also has the comparatively better time complexity and computing efficiency than some popular classifiers, especially for multiclass classification problems.

AB - Big data classification problems have drawn great attention from diverse fields, and many classifiers have been developed. Among those classifiers, the extended belief rule-based system (EBRBS) has shown its potential in both big data and multiclass situations, while the time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed first in this paper to decrease the time complexity and computing efficiency of EBRBS for multiclass classification under the assumption of large amount of data, including the strategy to skip rule weight calculation, a simplified evidential reasoning algorithm, and the domain division-based rule reduction method. This turns out to be a micro version of the EBRBS, called Micro-EBRBS. Moreover, one of commonly used cluster computing, named Apache Spark, is then applied to implement the parallel rule generation and inference schemes of the Micro-EBRBS for big data multiclass classification problems. The comparative analyses of experimental studies demonstrate that the Micro-EBRBS not only can obtain a desired accuracy but also has the comparatively better time complexity and computing efficiency than some popular classifiers, especially for multiclass classification problems.

KW - Apache spark

KW - big data

KW - extended belief rule-based system (EBRBS)

KW - multiclass

UR - https://pure.ulster.ac.uk/en/publications/a-micro-extended-belief-rule-based-system-for-big-data-multi-clas

UR - http://www.scopus.com/inward/record.url?scp=85055686291&partnerID=8YFLogxK

U2 - 10.1109/TSMC.2018.2872843

DO - 10.1109/TSMC.2018.2872843

M3 - Article

VL - 51

SP - 420

EP - 440

JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

SN - 2168-2216

IS - 1

ER -

A Micro-Extended Belief Rule-Based System for Big Data Multi-Class Classification Problems

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this