Abstract
| Language | English |
|---|---|
| Pages | 1-21 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
| DOIs | |
| Publication status | Accepted/In press - 28 Oct 2018 |
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A Micro-Extended Belief Rule-Based System for Big Data Multi-Class Classification Problems. / Yang, Longhao; Liu, J.; Wang, Yingming; Martinez, Luis.
In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 28.10.2018, p. 1-21.Research output: Contribution to journal › Article
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
PY - 2018/10/28
Y1 - 2018/10/28
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 multi-class situations while time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed firstly in the present paper to decrease the time complexity and computing efficiency of EBRBS for multi-class 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, classed 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 multi- class 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 multi-class 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 multi-class situations while time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed firstly in the present paper to decrease the time complexity and computing efficiency of EBRBS for multi-class 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, classed 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 multi- class 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 multi-class classification problems.
U2 - 10.1109/TSMC.2018.2872843
DO - 10.1109/TSMC.2018.2872843
M3 - Article
SP - 1
EP - 21
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
T2 - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
SN - 2168-2216
ER -