Abstract
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.
Highlights
The development of a powerful and accurate classifier is considered as one of the most significant and challenging tasks in machine learning and data mining [3]
Self-labeled techniques constitute a significant family of classification methods which progressively classify unlabeled data based on the most confident predictions and utilize them to modify the hypothesis learned from labeled samples
We focus our attention to Selftraining, Co-training and Tri-training which constitute the most efficient and commonly used self-labeled methods [21, 20, 22, 35, 37, 36]
Summary
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. 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. Povzetek: Razvit je nov delno nadzorovani ucni algoritem s pomocjo ansamblov in glasovalno shemo na osnovi najvecje verjetnosti
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