CST-Voting - A semi-supervised ensemble method for classification problems |
G. Kostopoulos, I.E. Livieris, S. Kotsiantis and V. Tampakas. CST-Voting - A semi-supervised ensemble method for classification problems. Journal of Intelligent & Fuzzy Systems, 2017.
Abstract - Semi-supervised learning is an emerging subfield of machine learning, with a view to building efficient classifiers exploiting a limited pool of labeled data together with a large pool of unlabeled ones. Most of the studies regarding semi-supervised learning deal with classification problems, whose goal is to learn a function that maps an unlabeled instance into a finite number of classes. In this paper, a new semi-supervised classification algorithm, which is based on a voting methodology, is proposed. The term attributed to this ensemble method is called CST-Voting. Ensemble methods have been effectively applied in various scientific fields and often perform better than the individual classifiers from which they are |