Autonomous learning multi-model systems from data streams

Plamen Parvanov Angelov, Xiaowei Gu, Jose Principe

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

64 Dyfyniadau (Scopus)
40 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.

Iaith wreiddiolSaesneg
Rhif yr erthygl8093666
Tudalennau (o-i)2213-2224
Nifer y tudalennau12
CyfnodolynIEEE Transactions on Fuzzy Systems
Cyfrol26
Rhif cyhoeddi4
Dyddiad ar-lein cynnar01 Tach 2017
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Awst 2018
Cyhoeddwyd yn allanolIe

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