Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study | Soft Computing and Intelligent Information Systems

I. Triguero, S. García, and F. Herrera, Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study. Summary: I. Triguero, S. García, and F.Herrera, Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study. Semi-supervised classification methods tackle training sets with large amounts of unlabeled data and a small quantity of labeled data. Among other approaches, self-labeled techniques iteratively enlarge the labeled dataset accepting that their own predictions are correct. In this article we provide a survey of self-labeled methods, proposing a taxonomy and analyzing empirically their performance with a large number of datasets. Note is then taken of which models are the most promising and/or unexplored alternatives for practitioners of this growing field. Moreover, a semi-supervised learning software package has been developed, integrating analyzed methods and datasets. The experimentation is based on 55 standard c

Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study | Soft Computing and Intelligent Information Systems You are here Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study This Website contains complementary material to the paper: I. Triguero, S. García , and F. Herrera , Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study. Summary: Abstract Experimental Framework Data-sets partitions used in the paper Algorithms and parameters Results obtained Statistical Tests A Semi-Super

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