Using binary classifiers for one-class classification
In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-ag...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kang, Seokho [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:187 ; year:2022 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.eswa.2021.115920 |
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| 520 | |a In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. | ||
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10.1016/j.eswa.2021.115920 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001557.pica (DE-627)ELV055622704 (ELSEVIER)S0957-4174(21)01274-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Kang, Seokho verfasserin aut Using binary classifiers for one-class classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. One-against-rest Elsevier Ensemble learning Elsevier One-class classifier Elsevier One-class classification Elsevier Binary classifier Elsevier Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:187 year:2022 pages:0 https://doi.org/10.1016/j.eswa.2021.115920 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 187 2022 0 |
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10.1016/j.eswa.2021.115920 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001557.pica (DE-627)ELV055622704 (ELSEVIER)S0957-4174(21)01274-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Kang, Seokho verfasserin aut Using binary classifiers for one-class classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. One-against-rest Elsevier Ensemble learning Elsevier One-class classifier Elsevier One-class classification Elsevier Binary classifier Elsevier Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:187 year:2022 pages:0 https://doi.org/10.1016/j.eswa.2021.115920 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 187 2022 0 |
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10.1016/j.eswa.2021.115920 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001557.pica (DE-627)ELV055622704 (ELSEVIER)S0957-4174(21)01274-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Kang, Seokho verfasserin aut Using binary classifiers for one-class classification 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. One-against-rest Elsevier Ensemble learning Elsevier One-class classifier Elsevier One-class classification Elsevier Binary classifier Elsevier Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:187 year:2022 pages:0 https://doi.org/10.1016/j.eswa.2021.115920 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 187 2022 0 |
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Using binary classifiers for one-class classification |
| abstract |
In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. |
| abstractGer |
In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. |
| abstract_unstemmed |
In this paper, we propose a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification. Given a training set comprising of only target class instances, it is partitioned into several clusters. Multiple binary classifiers are then trained with the clusters in a one-against-rest fashion, in which each classifier treats one cluster as a pseudo non-target class and is responsible for distinguishing the cluster from the other clusters. The binary classifiers are finally combined to constitute a one-class classifier, which is used to classify unknown instances. BCE-OC allows the use of any supervised classification algorithms for one-class classification. Accordingly, it allows extensive comparison of various learning algorithms to obtain a more competent one-class classifier for the problem. The effectiveness of BCE-OC is demonstrated through experimental validation using benchmark datasets. |
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GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Using binary classifiers for one-class classification |
| url |
https://doi.org/10.1016/j.eswa.2021.115920 |
| remote_bool |
true |
| ppnlink |
ELV000222070 |
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| hochschulschrift_bool |
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| doi_str |
10.1016/j.eswa.2021.115920 |
| up_date |
2024-07-06T18:03:40.034Z |
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| score |
7.4007006 |