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Self-Trained LMT for Semisupervised Learning
Joint Authors
Karlos, Stamatis
Fazakis, Nikos
Kotsiantis, Sotiris
Sgarbas, Kyriakos
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-29
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The most important asset of semisupervised classification methods is the use of available unlabeled data combined with a clearly smaller set of labeled examples, so as to increase the classification accuracy compared with the default procedure of supervised methods, which on the other hand use only the labeled data during the training phase.
Both the absence of automated mechanisms that produce labeled data and the high cost of needed human effort for completing the procedure of labelization in several scientific domains rise the need for semisupervised methods which counterbalance this phenomenon.
In this work, a self-trained Logistic Model Trees (LMT) algorithm is presented, which combines the characteristics of Logistic Trees under the scenario of poor available labeled data.
We performed an in depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique had better accuracy in most cases.
American Psychological Association (APA)
Fazakis, Nikos& Karlos, Stamatis& Kotsiantis, Sotiris& Sgarbas, Kyriakos. 2015. Self-Trained LMT for Semisupervised Learning. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099638
Modern Language Association (MLA)
Fazakis, Nikos…[et al.]. Self-Trained LMT for Semisupervised Learning. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1099638
American Medical Association (AMA)
Fazakis, Nikos& Karlos, Stamatis& Kotsiantis, Sotiris& Sgarbas, Kyriakos. Self-Trained LMT for Semisupervised Learning. Computational Intelligence and Neuroscience. 2015. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1099638
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099638