TY - JOUR
T1 - Autonomous learning multi-model systems from data streams
AU - Angelov, Plamen Parvanov
AU - Gu, Xiaowei
AU - Principe, Jose
N1 - Funding Information:
Manuscript received August 16, 2017; revised October 13, 2017; accepted October 23, 2017. Date of publication November 1, 2017; date of current version August 2, 2018. This work was supported by The Royal Society under Grant IE 141329/2014 “Novel Machine Learning Paradigms to address Big Data Streams.” (Corresponding author: Xiaowei Gu.) P. P. Angelov is with the School of Computing and Communications, Lancaster University, Lancaster LA1 4WA, U.K., and also holds an honorary position with the Technical University, Sofia, Bulgaria (e-mail: [email protected]).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - 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.
AB - 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.
KW - AnYa-type fuzzy-rule-based (FRB) system
KW - autonomous learning systems (ALSs)
KW - classification
KW - data clouds
KW - empirical data analytics (EDA)
KW - nonparametric
KW - prediction
UR - http://www.research.lancs.ac.uk/portal/en/publications/autonomous-learning-multimodel-systems-from-data-streams(2fc285c3-566b-4c0d-9862-a2171570e7a6).html
UR - http://www.scopus.com/inward/record.url?scp=85032800380&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2017.2769039
DO - 10.1109/TFUZZ.2017.2769039
M3 - Article
SN - 1063-6706
VL - 26
SP - 2213
EP - 2224
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 4
M1 - 8093666
ER -