Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams
Data stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for class...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6147378 |
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| _version_ | 1841524412852994048 |
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| author | Sanmin Liu Shan Xue Fanzhen Liu Jieren Cheng Xiulai Li Chao Kong Jia Wu |
| author_facet | Sanmin Liu Shan Xue Fanzhen Liu Jieren Cheng Xiulai Li Chao Kong Jia Wu |
| author_sort | Sanmin Liu |
| collection | DOAJ |
| description | Data stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for classifying nonstationary data streams with noise. Our approach integrates three strategies: incremental learning to monitor and adapt to concept drift; ensemble learning to improve model stability; and a microclustering procedure that distinguishes drift from noise and predicts the labels of incoming instances via majority vote. Experiments with two synthetic datasets designed to test for both gradual and abrupt drift show that our method provides more accurate classification in nonstationary data streams with noise than the two popular baselines. |
| format | Article |
| id | doaj-art-b561ad5e426b4e7e9c82b43b4c77dac0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b561ad5e426b4e7e9c82b43b4c77dac02025-02-03T05:53:22ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/61473786147378Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data StreamsSanmin Liu0Shan Xue1Fanzhen Liu2Jieren Cheng3Xiulai Li4Chao Kong5Jia Wu6School of Computer and Information, Anhui Polytechnic University, Wuhu 241000, ChinaDepartment of Computing, Macquarie University, Sydney 2109, AustraliaDepartment of Computing, Macquarie University, Sydney 2109, AustraliaSchool of Computer Science & Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Computer Science & Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Computer and Information, Anhui Polytechnic University, Wuhu 241000, ChinaDepartment of Computing, Macquarie University, Sydney 2109, AustraliaData stream classification becomes a promising prediction work with relevance to many practical environments. However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. Hence, a new incremental ensemble model is presented for classifying nonstationary data streams with noise. Our approach integrates three strategies: incremental learning to monitor and adapt to concept drift; ensemble learning to improve model stability; and a microclustering procedure that distinguishes drift from noise and predicts the labels of incoming instances via majority vote. Experiments with two synthetic datasets designed to test for both gradual and abrupt drift show that our method provides more accurate classification in nonstationary data streams with noise than the two popular baselines.http://dx.doi.org/10.1155/2020/6147378 |
| spellingShingle | Sanmin Liu Shan Xue Fanzhen Liu Jieren Cheng Xiulai Li Chao Kong Jia Wu Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams Complexity |
| title | Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams |
| title_full | Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams |
| title_fullStr | Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams |
| title_full_unstemmed | Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams |
| title_short | Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams |
| title_sort | microcluster based incremental ensemble learning for noisy nonstationary data streams |
| url | http://dx.doi.org/10.1155/2020/6147378 |
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