FP-ELM: An online sequential learning algorithm for dealing with concept drift

The online sequential extreme learning machine (OS-ELM) algorithm is an on-line and incremental learning method, which can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. And OS-ELM achieves the same learning performance as ELM trained by the complete set of data. However...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 207; pp. 322 - 334
Main Authors: Liu, Dong, Wu, YouXi, Jiang, He
Format: Journal Article
Language:English
Published: Elsevier B.V 26.09.2016
Subjects:
Concept drift
Extreme learning machine
Online/incremental learning
Regularized optimization method
Online/incremental learning
Extreme learning machine
Concept drift
Regularized optimization method
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:The online sequential extreme learning machine (OS-ELM) algorithm is an on-line and incremental learning method, which can learn data one-by-one or chunk-by-chunk with a fixed or varying chunk size. And OS-ELM achieves the same learning performance as ELM trained by the complete set of data. However, in on-line learning environments, the concepts to be learned may change with time, a feature referred to as concept drift. To use ELMs in such non-stationary environments, a forgetting parameters extreme learning machine (FP-ELM) is proposed in this paper. The proposed FP-ELM can achieve incremental and on-line learning, just like OS-ELM. Furthermore, FP-ELM will assign a forgetting parameter to the previous training data according to the current performance to adapt to possible changes after a new chunk comes. The regularized optimization method is used to avoid estimator windup. Performance comparisons between FP-ELM and two frequently used ensemble approaches are carried out on several regression and classification problems with concept drift. The experimental results show that FP-ELM produces comparable or better performance with lower training time.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.04.043