Self-Organizing Fuzzy Inference Ensemble System for Big Streaming Data Classication

Gu, Xiaowei and Angelov, Plamen and Zhao, Zhijin (2021) Self-Organizing Fuzzy Inference Ensemble System for Big Streaming Data Classication. Knowledge-Based Systems. ISSN 0950-7051 (In Press)

[img]
Text (SOFEnsemble)
SOFEnsemble.pdf - Accepted Version
Restricted to Repository staff only until 1 January 2050.
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (645kB)

Abstract

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large- scale, complex data streams are often limited. To address this deciency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy in- ference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its de- cision boundaries based on the inter-class and intra-class distances between pro- totypes identied from successive data chunks for higher classication precision. Thanks to its parallel distributed computing architecture, the proposed ensem- ble framework can achieve great classication precision while maintain high computational eciency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classication accuracy and computational eciency.

Item Type:
Journal Article
Journal or Publication Title:
Knowledge-Based Systems
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1802
Subjects:
ID Code:
151781
Deposited By:
Deposited On:
17 Feb 2021 13:30
Refereed?:
Yes
Published?:
In Press
Last Modified:
17 Feb 2021 13:30