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In this paper we propose a novel semi-supervised learning algorithm, called Random Split Statistic algorithm (RSSalg), designed to exploit the advantages of cotraining algorithm, while being exempt from co-training requirement for the existence of adequate feature split in the dataset. In our method, co-training algorithm is run for a predefined number of times, using a different random split of features in each run. Each run of co-training produces a different enlarged training set, consistingdoi:10.12700/aph.10.02.2013.2.10. fatcat:rnmxjpsk5nctffovkafrrtcnce