A New Ensemble Self-labeled Semi-supervised Algorithm

Ioannis E. Livieris


As an alternative to traditional classification methods, semi-supervised learning algorithms have become a hot topic of significant research, exploiting the knowledge hidden in the unlabeled data for building powerful and effective classifiers. In this work, a new ensemble-based semi-supervised algorithm is proposed which is based on a maximum-probability voting scheme. The reported numerical results illustrate the efficacy of the proposed algorithm outperforming classical semi-supervised algorithms in term of classification accuracy, leading to more efficient and robust predictive models.

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DOI: https://doi.org/10.31449/inf.v43i2.2217

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