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A new ensemble self-labeled semi-supervised algorithm
2019
Informatica (Ljubljana, Tiskana izd.)
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
doi:10.31449/inf.v43i2.2217
fatcat:skxctlhzwjhzrooprj7qxxnaxu