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Autonomous learning multi-model systems from data streams

Research output: Contribution to journalJournal article

Published

Standard

Autonomous learning multi-model systems from data streams. / Angelov, Plamen Parvanov; Gu, Xiaowei; Principe, Jose .

In: IEEE Transactions on Fuzzy Systems, Vol. 26, No. 4, 01.08.2018, p. 2213-2224.

Research output: Contribution to journalJournal article

Harvard

Angelov, PP, Gu, X & Principe, J 2018, 'Autonomous learning multi-model systems from data streams', IEEE Transactions on Fuzzy Systems, vol. 26, no. 4, pp. 2213-2224. https://doi.org/10.1109/TFUZZ.2017.2769039

APA

Angelov, P. P., Gu, X., & Principe, J. (2018). Autonomous learning multi-model systems from data streams. IEEE Transactions on Fuzzy Systems, 26(4), 2213-2224. https://doi.org/10.1109/TFUZZ.2017.2769039

Vancouver

Angelov PP, Gu X, Principe J. Autonomous learning multi-model systems from data streams. IEEE Transactions on Fuzzy Systems. 2018 Aug 1;26(4):2213-2224. https://doi.org/10.1109/TFUZZ.2017.2769039

Author

Angelov, Plamen Parvanov ; Gu, Xiaowei ; Principe, Jose . / Autonomous learning multi-model systems from data streams. In: IEEE Transactions on Fuzzy Systems. 2018 ; Vol. 26, No. 4. pp. 2213-2224.

Bibtex

@article{2fc285c3566b4c0d9862a2171570e7a6,
title = "Autonomous learning multi-model systems from data streams",
abstract = "In this paper, an approach to autonomous learning of a multi-model 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 multi-model 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 meta-parameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory- and calculation-efficiency 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.",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu and Jose Principe",
note = "{\textcopyright}2017 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.",
year = "2018",
month = aug
day = "1",
doi = "10.1109/TFUZZ.2017.2769039",
language = "English",
volume = "26",
pages = "2213--2224",
journal = "IEEE Transactions on Fuzzy Systems",
issn = "1063-6706",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "4",

}

RIS

TY - JOUR

T1 - Autonomous learning multi-model systems from data streams

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

AU - Principe, Jose

N1 - ©2017 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.

PY - 2018/8/1

Y1 - 2018/8/1

N2 - In this paper, an approach to autonomous learning of a multi-model 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 multi-model 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 meta-parameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory- and calculation-efficiency 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 multi-model 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 multi-model 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 meta-parameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory- and calculation-efficiency 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.

U2 - 10.1109/TFUZZ.2017.2769039

DO - 10.1109/TFUZZ.2017.2769039

M3 - Journal article

VL - 26

SP - 2213

EP - 2224

JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

SN - 1063-6706

IS - 4

ER -