Engineering Applications of Neural Networks pp 139-148
Self-Train LogitBoost for Semi-supervised Learning
- Cite this paper as:
- Karlos S., Fazakis N., Kotsiantis S., Sgarbas K. (2015) Self-Train LogitBoost for Semi-supervised Learning. In: Iliadis L., Jayne C. (eds) Engineering Applications of Neural Networks. Communications in Computer and Information Science, vol 517. Springer, Cham
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.
KeywordsSemi-supervised learning Logitboost Classification method Labeled and/or unlabeled data
Unable to display preview. Download preview PDF.