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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Self-Train LogitBoost for Semi-supervised Learning
verfasst von : Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, Kyriakos Sgarbas
Erschienen in: Engineering Applications of Neural Networks
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Semi-supervised classification methods are based on the use of unlabeled data in combination with a smaller set of labeled examples, in order to increase the classification rate compared with the supervised methods, in which the total training is executed only by the usage of labeled data. In this work, a self-train Logitboost algorithm is presented. The self-train process improves the results by using the accurate class probabilities for which the Logitboost regression tree model is more confident at the unlabeled instances. We performed a comparison with other well-known semi-supervised classification methods on standard benchmark datasets and the presented technique had better accuracy in most cases.