Machine learning for balanced and imbalanced data
Recently, a kernel based online sequential extreme learning machine (OS-ELM) methods, OS-ELM with kernels (OS-ELMK) for non-stationary time series prediction is proposed. However, OS-ELMK has not been applied to classification problems and it is not clear which OS-ELM based method is more effective...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/67557 |
| _version_ | 1826109785162907648 |
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| author | Ding, Shuya |
| author2 | Lin Zhiping |
| author_facet | Lin Zhiping Ding, Shuya |
| author_sort | Ding, Shuya |
| collection | NTU |
| description | Recently, a kernel based online sequential extreme learning machine (OS-ELM) methods, OS-ELM with kernels (OS-ELMK) for non-stationary time series prediction is proposed. However, OS-ELMK has not been applied to classification problems and it is not clear which OS-ELM based method is more effective as a classifier. In this project, OS-ELM is extended to classification problems with relatively balanced datasets and compared with other OS-ELM methods. It is the first kernel-based OS-ELM classifier which can learn in both chunk-by-chunk and one-by-one modes. Guidelines for selecting appropriate OS-ELM classifier for different applications are also provided.
Moreover, by combining OS-ELMK’s implicit feature mapping and a cost sensitive weighting scheme from weighted OS-ELM (WOS-ELM), a new kernel based online sequential method is proposed for imbalanced data classification. The new method is referred to as weighted OS-ELM with kernels (WOS-ELMK). The performance of WOS-ELMK is evaluated on benchmark imbalanced datasets and compared with a recently proposed voting based WOS-ELM (VWOS-ELM) method. |
| first_indexed | 2024-10-01T02:23:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/67557 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:23:37Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/675572023-07-07T16:05:53Z Machine learning for balanced and imbalanced data Ding, Shuya Lin Zhiping School of Electrical and Electronic Engineering DRNTU::Engineering Recently, a kernel based online sequential extreme learning machine (OS-ELM) methods, OS-ELM with kernels (OS-ELMK) for non-stationary time series prediction is proposed. However, OS-ELMK has not been applied to classification problems and it is not clear which OS-ELM based method is more effective as a classifier. In this project, OS-ELM is extended to classification problems with relatively balanced datasets and compared with other OS-ELM methods. It is the first kernel-based OS-ELM classifier which can learn in both chunk-by-chunk and one-by-one modes. Guidelines for selecting appropriate OS-ELM classifier for different applications are also provided. Moreover, by combining OS-ELMK’s implicit feature mapping and a cost sensitive weighting scheme from weighted OS-ELM (WOS-ELM), a new kernel based online sequential method is proposed for imbalanced data classification. The new method is referred to as weighted OS-ELM with kernels (WOS-ELMK). The performance of WOS-ELMK is evaluated on benchmark imbalanced datasets and compared with a recently proposed voting based WOS-ELM (VWOS-ELM) method. Bachelor of Engineering 2016-05-18T03:41:06Z 2016-05-18T03:41:06Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67557 en Nanyang Technological University 49 p. application/pdf |
| spellingShingle | DRNTU::Engineering Ding, Shuya Machine learning for balanced and imbalanced data |
| title | Machine learning for balanced and imbalanced data |
| title_full | Machine learning for balanced and imbalanced data |
| title_fullStr | Machine learning for balanced and imbalanced data |
| title_full_unstemmed | Machine learning for balanced and imbalanced data |
| title_short | Machine learning for balanced and imbalanced data |
| title_sort | machine learning for balanced and imbalanced data |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/67557 |
| work_keys_str_mv | AT dingshuya machinelearningforbalancedandimbalanceddata |