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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -