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Autonomous Learning Multimodel Systems From Data Streams

Angelov, Plamen P., Gu, Xiaowei, Principe, Jose C. (2018) Autonomous Learning Multimodel Systems From Data Streams. IEEE Transactions on Fuzzy Systems, 26 (4). pp. 2213-2224. ISSN 1063-6706. (doi:10.1109/TFUZZ.2017.2769039) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90115)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/TFUZZ.2017.2769039

Abstract

In this paper, an approach to autonomous learning of a multimodel system from streaming data, named ALMMo, is proposed. The proposed approach is generic and can easily be applied also to probabilistic or other types of local models forming multimodel systems. It is fully data driven and its structure is decided by the nonparametric data clouds extracted from the empirically observed data without making any prior assumptions concerning data distribution and other data properties. All metaparameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory and calculation efficiencies of the proposed algorithm. The structural evolution mechanism and online data cloud quality monitoring mechanism of the ALMMo system largely enhance the ability of handling shifts and/or drifts in the streaming data pattern. Numerical examples of the use of ALMMo system for streaming data analytics, classification, and prediction are presented as a proof of the proposed concept.

Item Type: Article
DOI/Identification number: 10.1109/TFUZZ.2017.2769039
Uncontrolled keywords: Clouds; Data analysis; Learning systems; Data models; Meteorology; Computational modeling; Wavelet transforms; AnYa-type fuzzy-rule-based (FRB) system; autonomous learning systems (ALSs); classification; data clouds; empirical data analytics (EDA); nonparametric; prediction
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 10 Sep 2021 09:10 UTC
Last Modified: 13 Sep 2021 11:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90115 (The current URI for this page, for reference purposes)
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