Autonomous learning multi-model systems from data streams

Plamen Parvanov Angelov, Xiaowei Gu, Jose Principe

Research output: Contribution to journalArticlepeer-review

59 Citations (SciVal)
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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 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.

Original languageEnglish
Article number8093666
Pages (from-to)2213-2224
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Volume26
Issue number4
Early online date01 Nov 2017
DOIs
Publication statusPublished - 01 Aug 2018
Externally publishedYes

Keywords

  • AnYa-type fuzzy-rule-based (FRB) system
  • autonomous learning systems (ALSs)
  • classification
  • data clouds
  • empirical data analytics (EDA)
  • nonparametric
  • prediction

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