Autonomous Learning Multimodel Systems From Data Streams

Angelov, Plamen P. and Gu, Xiaowei and Principe, Jose C. (2018) Autonomous Learning Multimodel Systems From Data Streams. IEEE Transactions on Fuzzy Systems, 26 (4). pp. 2213-2224. ISSN 1063-6706

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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 he 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 he 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: Journal Article
Journal or Publication Title: IEEE Transactions on Fuzzy Systems
Additional Information: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2200/2207
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 131155
Deposited By: ep_importer_pure
Deposited On: 25 Nov 2019 16:20
Refereed?: Yes
Published?: Published
Last Modified: 27 Feb 2020 04:45
URI: https://eprints.lancs.ac.uk/id/eprint/131155

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