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CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem.

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Title: CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem.
Authors: Podolak, Igor1 igor.podolak@uj.edu.pl, Roman, Adam1
Source: Pattern Analysis & Applications. Nov2011, Vol. 14 Issue 4, p395-413. 19p.
Subjects: Pattern perception, Classifiers (Linguistics), Algorithms, Document clustering, Artificial neural networks, Face perception
Abstract: This paper describes in full detail a model of a hierarchical classifier (HC). The original classification problem is broken down into several subproblems and a weak classifier is built for each of them. Subproblems consist of examples from a subset of the whole set of output classes. It is essential for this classification framework that the generated subproblems would overlap, i.e. some individual classes could belong to more than one subproblem. This approach allows to reduce the overall risk. Individual classifiers built for the subproblems are weak, i.e. their accuracy is only a little better than the accuracy of a random classifier. The notion of weakness for a multiclass model is extended in this paper. It is more intuitive than approaches proposed so far. In the HC model described, after a single node is trained, its problem is split into several subproblems using a clustering algorithm. It is responsible for selecting classes similarly classified. The main scope of this paper is focused on finding the most appropriate clustering method. Some algorithms are defined and compared. Finally, we compare a whole HC with other machine learning approaches. [ABSTRACT FROM AUTHOR]
Copyright of Pattern Analysis & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Pattern+perception%22">Pattern perception</searchLink><br /><searchLink fieldCode="DE" term="%22Classifiers+%28Linguistics%29%22">Classifiers (Linguistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Document+clustering%22">Document clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Face+perception%22">Face perception</searchLink>
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  Data: This paper describes in full detail a model of a hierarchical classifier (HC). The original classification problem is broken down into several subproblems and a weak classifier is built for each of them. Subproblems consist of examples from a subset of the whole set of output classes. It is essential for this classification framework that the generated subproblems would overlap, i.e. some individual classes could belong to more than one subproblem. This approach allows to reduce the overall risk. Individual classifiers built for the subproblems are weak, i.e. their accuracy is only a little better than the accuracy of a random classifier. The notion of weakness for a multiclass model is extended in this paper. It is more intuitive than approaches proposed so far. In the HC model described, after a single node is trained, its problem is split into several subproblems using a clustering algorithm. It is responsible for selecting classes similarly classified. The main scope of this paper is focused on finding the most appropriate clustering method. Some algorithms are defined and compared. Finally, we compare a whole HC with other machine learning approaches. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Pattern Analysis & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10044-011-0204-3
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      – Code: eng
        Text: English
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      – SubjectFull: Pattern perception
        Type: general
      – SubjectFull: Classifiers (Linguistics)
        Type: general
      – SubjectFull: Algorithms
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      – SubjectFull: Document clustering
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Face perception
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      – TitleFull: CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem.
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              Text: Nov2011
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