Kernel based online learning for imbalance multiclass classification
In this paper, we propose a weighted online sequential extreme learning machine with kernels (WOS-ELMK) for class imbalance learning (CIL). The existing online sequential extreme learning machine (OS-ELM) methods for CIL use random feature mapping. WOS-ELMK is the first OS-ELM method which uses kern...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/138810 |
| _version_ | 1826110363766095872 |
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| author | Ding, Shuya Mirza, Bilal Lin, Zhiping Cao, Jiuwen Lai, Xiaoping Nguyen, Tam Van Sepulveda, Jose |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ding, Shuya Mirza, Bilal Lin, Zhiping Cao, Jiuwen Lai, Xiaoping Nguyen, Tam Van Sepulveda, Jose |
| author_sort | Ding, Shuya |
| collection | NTU |
| description | In this paper, we propose a weighted online sequential extreme learning machine with kernels (WOS-ELMK) for class imbalance learning (CIL). The existing online sequential extreme learning machine (OS-ELM) methods for CIL use random feature mapping. WOS-ELMK is the first OS-ELM method which uses kernel mapping for online class imbalance learning. The kernel mapping avoids the non-optimal hidden node problem associated with weighted OS-ELM (WOS-ELM) and other existing OS-ELM methods for CIL. WOS-ELMK tackles both the binary class and multiclass imbalance problems in one-by-one as well as chunk-by-chunk learning modes. For imbalanced big data streams, a fixed size window scheme is also implemented for WOS-ELMK. We empirically show that WOS-ELMK obtains superior performance in general than some recently proposed CIL approaches on 17 binary class and 8 multiclass imbalanced datasets. |
| first_indexed | 2024-10-01T02:33:11Z |
| format | Journal Article |
| id | ntu-10356/138810 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:33:11Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1388102020-05-13T02:16:07Z Kernel based online learning for imbalance multiclass classification Ding, Shuya Mirza, Bilal Lin, Zhiping Cao, Jiuwen Lai, Xiaoping Nguyen, Tam Van Sepulveda, Jose School of Electrical and Electronic Engineering Engineering::Computer science and engineering Class Imbalance Extreme Learning Machine (ELM) In this paper, we propose a weighted online sequential extreme learning machine with kernels (WOS-ELMK) for class imbalance learning (CIL). The existing online sequential extreme learning machine (OS-ELM) methods for CIL use random feature mapping. WOS-ELMK is the first OS-ELM method which uses kernel mapping for online class imbalance learning. The kernel mapping avoids the non-optimal hidden node problem associated with weighted OS-ELM (WOS-ELM) and other existing OS-ELM methods for CIL. WOS-ELMK tackles both the binary class and multiclass imbalance problems in one-by-one as well as chunk-by-chunk learning modes. For imbalanced big data streams, a fixed size window scheme is also implemented for WOS-ELMK. We empirically show that WOS-ELMK obtains superior performance in general than some recently proposed CIL approaches on 17 binary class and 8 multiclass imbalanced datasets. 2020-05-13T02:16:07Z 2020-05-13T02:16:07Z 2017 Journal Article Ding, S., Mirza, B., Lin, Z., Cao, J., Lai, X., Nguyen, T. V., & Sepulveda, J. (2018). Kernel based online learning for imbalance multiclass classification. Neurocomputing, 277, 139-148. doi:10.1016/j.neucom.2017.02.102 0925-2312 https://hdl.handle.net/10356/138810 10.1016/j.neucom.2017.02.102 2-s2.0-85028702271 277 139 148 en Neurocomputing © 2017 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Class Imbalance Extreme Learning Machine (ELM) Ding, Shuya Mirza, Bilal Lin, Zhiping Cao, Jiuwen Lai, Xiaoping Nguyen, Tam Van Sepulveda, Jose Kernel based online learning for imbalance multiclass classification |
| title | Kernel based online learning for imbalance multiclass classification |
| title_full | Kernel based online learning for imbalance multiclass classification |
| title_fullStr | Kernel based online learning for imbalance multiclass classification |
| title_full_unstemmed | Kernel based online learning for imbalance multiclass classification |
| title_short | Kernel based online learning for imbalance multiclass classification |
| title_sort | kernel based online learning for imbalance multiclass classification |
| topic | Engineering::Computer science and engineering Class Imbalance Extreme Learning Machine (ELM) |
| url | https://hdl.handle.net/10356/138810 |
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