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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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 207; s. 322 - 334 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
26.09.2016
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Jiang, He Wu, YouXi Liu, Dong |
| Author_xml | – sequence: 1 givenname: Dong surname: Liu fullname: Liu, Dong email: dongliu05@gmail.com organization: School of Software, Dalian University of Technology, Dalian 116621, China – sequence: 2 givenname: YouXi surname: Wu fullname: Wu, YouXi email: wuc@scse.hebut.edu.cn organization: School of Computer Science and Software, Hebei University of Technology, Tianjin 300130, China – sequence: 3 givenname: He surname: Jiang fullname: Jiang, He email: jianghe@dlut.edu.cn organization: School of Software, Dalian University of Technology, Dalian 116621, China |
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