Online Ensemble Learning of Data Streams with Gradually Evolved Classes
Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes em...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Y [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on knowledge and data engineering - New York, NY : IEEE, 1989, (2016) |
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| Übergeordnetes Werk: |
year:2016 |
| Links: |
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| DOI / URN: |
10.1109/TKDE.2016.2526675 |
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OLC1974106888 |
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| 520 | |a Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print | ||
| 540 | |a Nutzungsrecht: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. | ||
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10.1109/TKDE.2016.2526675 doi PQ20160430 (DE-627)OLC1974106888 (DE-599)GBVOLC1974106888 (PRQ)le_dspace_oai_lra_le_ac_uk_2381_367210 (KEY)0175400920160000000000000000onlineensemblelearningofdatastreamswithgraduallyev DE-627 ger DE-627 rakwb eng 620 530 000 DNB SA 5571 AVZ rvk 54.00 bkl Sun, Y verfasserin aut Online Ensemble Learning of Data Streams with Gradually Evolved Classes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print Nutzungsrecht: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. Data mininG Adaptation models data stream mining Transient analysis Probability distribution Sun class evolution imbalanced classification Computer science ensemble model Data models on-line learning Tang, K oth Minku, Leandro Lei oth Wang, S oth Yao, X oth Enthalten in IEEE transactions on knowledge and data engineering New York, NY : IEEE, 1989 (2016) (DE-627)130765732 (DE-600)1001468-8 (DE-576)023036060 1041-4347 nnns year:2016 http://dx.doi.org/10.1109/TKDE.2016.2526675 Volltext http://hdl.handle.net/2381/36721 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-BBI SA 5571 54.00 AVZ AR 2016 |
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10.1109/TKDE.2016.2526675 doi PQ20160430 (DE-627)OLC1974106888 (DE-599)GBVOLC1974106888 (PRQ)le_dspace_oai_lra_le_ac_uk_2381_367210 (KEY)0175400920160000000000000000onlineensemblelearningofdatastreamswithgraduallyev DE-627 ger DE-627 rakwb eng 620 530 000 DNB SA 5571 AVZ rvk 54.00 bkl Sun, Y verfasserin aut Online Ensemble Learning of Data Streams with Gradually Evolved Classes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print Nutzungsrecht: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. Data mininG Adaptation models data stream mining Transient analysis Probability distribution Sun class evolution imbalanced classification Computer science ensemble model Data models on-line learning Tang, K oth Minku, Leandro Lei oth Wang, S oth Yao, X oth Enthalten in IEEE transactions on knowledge and data engineering New York, NY : IEEE, 1989 (2016) (DE-627)130765732 (DE-600)1001468-8 (DE-576)023036060 1041-4347 nnns year:2016 http://dx.doi.org/10.1109/TKDE.2016.2526675 Volltext http://hdl.handle.net/2381/36721 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-BBI SA 5571 54.00 AVZ AR 2016 |
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10.1109/TKDE.2016.2526675 doi PQ20160430 (DE-627)OLC1974106888 (DE-599)GBVOLC1974106888 (PRQ)le_dspace_oai_lra_le_ac_uk_2381_367210 (KEY)0175400920160000000000000000onlineensemblelearningofdatastreamswithgraduallyev DE-627 ger DE-627 rakwb eng 620 530 000 DNB SA 5571 AVZ rvk 54.00 bkl Sun, Y verfasserin aut Online Ensemble Learning of Data Streams with Gradually Evolved Classes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print Nutzungsrecht: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. Data mininG Adaptation models data stream mining Transient analysis Probability distribution Sun class evolution imbalanced classification Computer science ensemble model Data models on-line learning Tang, K oth Minku, Leandro Lei oth Wang, S oth Yao, X oth Enthalten in IEEE transactions on knowledge and data engineering New York, NY : IEEE, 1989 (2016) (DE-627)130765732 (DE-600)1001468-8 (DE-576)023036060 1041-4347 nnns year:2016 http://dx.doi.org/10.1109/TKDE.2016.2526675 Volltext http://hdl.handle.net/2381/36721 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-BBI SA 5571 54.00 AVZ AR 2016 |
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10.1109/TKDE.2016.2526675 doi PQ20160430 (DE-627)OLC1974106888 (DE-599)GBVOLC1974106888 (PRQ)le_dspace_oai_lra_le_ac_uk_2381_367210 (KEY)0175400920160000000000000000onlineensemblelearningofdatastreamswithgraduallyev DE-627 ger DE-627 rakwb eng 620 530 000 DNB SA 5571 AVZ rvk 54.00 bkl Sun, Y verfasserin aut Online Ensemble Learning of Data Streams with Gradually Evolved Classes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print Nutzungsrecht: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. Data mininG Adaptation models data stream mining Transient analysis Probability distribution Sun class evolution imbalanced classification Computer science ensemble model Data models on-line learning Tang, K oth Minku, Leandro Lei oth Wang, S oth Yao, X oth Enthalten in IEEE transactions on knowledge and data engineering New York, NY : IEEE, 1989 (2016) (DE-627)130765732 (DE-600)1001468-8 (DE-576)023036060 1041-4347 nnns year:2016 http://dx.doi.org/10.1109/TKDE.2016.2526675 Volltext http://hdl.handle.net/2381/36721 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-BBI SA 5571 54.00 AVZ AR 2016 |
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10.1109/TKDE.2016.2526675 doi PQ20160430 (DE-627)OLC1974106888 (DE-599)GBVOLC1974106888 (PRQ)le_dspace_oai_lra_le_ac_uk_2381_367210 (KEY)0175400920160000000000000000onlineensemblelearningofdatastreamswithgraduallyev DE-627 ger DE-627 rakwb eng 620 530 000 DNB SA 5571 AVZ rvk 54.00 bkl Sun, Y verfasserin aut Online Ensemble Learning of Data Streams with Gradually Evolved Classes 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print Nutzungsrecht: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. Data mininG Adaptation models data stream mining Transient analysis Probability distribution Sun class evolution imbalanced classification Computer science ensemble model Data models on-line learning Tang, K oth Minku, Leandro Lei oth Wang, S oth Yao, X oth Enthalten in IEEE transactions on knowledge and data engineering New York, NY : IEEE, 1989 (2016) (DE-627)130765732 (DE-600)1001468-8 (DE-576)023036060 1041-4347 nnns year:2016 http://dx.doi.org/10.1109/TKDE.2016.2526675 Volltext http://hdl.handle.net/2381/36721 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-BBI SA 5571 54.00 AVZ AR 2016 |
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620 530 000 DNB SA 5571 AVZ rvk 54.00 bkl Online Ensemble Learning of Data Streams with Gradually Evolved Classes Data mininG Adaptation models data stream mining Transient analysis Probability distribution Sun class evolution imbalanced classification Computer science ensemble model Data models on-line learning |
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IEEE transactions on knowledge and data engineering |
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Online Ensemble Learning of Data Streams with Gradually Evolved Classes |
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Online Ensemble Learning of Data Streams with Gradually Evolved Classes |
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Sun, Y |
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online ensemble learning of data streams with gradually evolved classes |
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Online Ensemble Learning of Data Streams with Gradually Evolved Classes |
| abstract |
Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print |
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Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print |
| abstract_unstemmed |
Class evolution, the phenomenon of class emergence and disappearance, is an important research topic for data stream mining. All previous studies implicitly regard class evolution as a transient change, which is not true for many real-world problems. This paper concerns the scenario where classes emerge or disappear gradually. A class-based ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By maintaining a base learner for each class and dynamically updating the base learners with new data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual evolution of classes. Empirical studies demonstrate the effectiveness of CBCE in various class evolution scenarios in comparison to existing class evolution adaptation methods. Peer-reviewed Post-print |
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| title_short |
Online Ensemble Learning of Data Streams with Gradually Evolved Classes |
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http://dx.doi.org/10.1109/TKDE.2016.2526675 http://hdl.handle.net/2381/36721 |
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Tang, K Minku, Leandro Lei Wang, S Yao, X |
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Tang, K Minku, Leandro Lei Wang, S Yao, X |
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