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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 originated. The efficiency of the proposed algorithm is compared to three familiar semi-supervised learning methods on a plethora of benchmark datasets using three representative supervised classifiers as base learners. 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