Sequential extreme learning machines for class imbalance and concept drift
Class imbalance and concept drift are two problems commonly exist in sequential learning. A weighted online sequential extreme learning machine (WOS-ELM) algorithm is proposed that has a distinctive feature of class imbalance learning (CIL) in both the chunk-by-chunk and one-by-one modes. A new samp...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2015
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/65290 |
| _version_ | 1811688998674890752 |
|---|---|
| author | Mirza Bilal |
| author2 | Lin Zhiping |
| author_facet | Lin Zhiping Mirza Bilal |
| author_sort | Mirza Bilal |
| collection | NTU |
| description | Class imbalance and concept drift are two problems commonly exist in sequential learning. A weighted online sequential extreme learning machine (WOS-ELM) algorithm is proposed that has a distinctive feature of class imbalance learning (CIL) in both the chunk-by-chunk and one-by-one modes. A new sample can update the classifier without waiting for a chunk to be completed. For CIL in drifting environments, a computationally efficient framework, referred to as ensemble of subset online sequential extreme learning machine is proposed. It comprises a main ensemble representing short-term memory, an information storage module representing long-term memory and a change detector to promptly detect concept drifts. A self-regulatory method, referred to as meta-cognitive online sequential extreme learning machine, is proposed to adapt the learning according to the nature of data stream i.e. select appropriate strategy for class imbalance and concept drift learning. A single OS-ELM equation is proposed for multiclass imbalance and concept drift learning. |
| first_indexed | 2024-10-01T05:41:07Z |
| format | Thesis |
| id | ntu-10356/65290 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:41:07Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/652902023-07-04T17:21:32Z Sequential extreme learning machines for class imbalance and concept drift Mirza Bilal Lin Zhiping School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Class imbalance and concept drift are two problems commonly exist in sequential learning. A weighted online sequential extreme learning machine (WOS-ELM) algorithm is proposed that has a distinctive feature of class imbalance learning (CIL) in both the chunk-by-chunk and one-by-one modes. A new sample can update the classifier without waiting for a chunk to be completed. For CIL in drifting environments, a computationally efficient framework, referred to as ensemble of subset online sequential extreme learning machine is proposed. It comprises a main ensemble representing short-term memory, an information storage module representing long-term memory and a change detector to promptly detect concept drifts. A self-regulatory method, referred to as meta-cognitive online sequential extreme learning machine, is proposed to adapt the learning according to the nature of data stream i.e. select appropriate strategy for class imbalance and concept drift learning. A single OS-ELM equation is proposed for multiclass imbalance and concept drift learning. DOCTOR OF PHILOSOPHY (EEE) 2015-07-01T01:45:00Z 2015-07-01T01:45:00Z 2015 2015 Thesis Mirza Bilal. (2015). Sequential extreme learning machines for class imbalance and concept drift. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/65290 10.32657/10356/65290 en 146 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Mirza Bilal Sequential extreme learning machines for class imbalance and concept drift |
| title | Sequential extreme learning machines for class imbalance and concept drift |
| title_full | Sequential extreme learning machines for class imbalance and concept drift |
| title_fullStr | Sequential extreme learning machines for class imbalance and concept drift |
| title_full_unstemmed | Sequential extreme learning machines for class imbalance and concept drift |
| title_short | Sequential extreme learning machines for class imbalance and concept drift |
| title_sort | sequential extreme learning machines for class imbalance and concept drift |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/65290 |
| work_keys_str_mv | AT mirzabilal sequentialextremelearningmachinesforclassimbalanceandconceptdrift |