A New Ensemble Semi-supervised Self-labeled Algorithm
A New Ensemble Semi-supervised Self-labeled Algorithm
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DOI：
10.31449/inf.v43i2.2217
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发表时间：
2019-06-01
影响因子：
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通讯作者：
Livieris, Ioannis
中科院分区：
文献类型：
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作者：
Livieris, Ioannis
半监督学习算法作为传统分类方法的替代方法，利用隐藏在未标记数据中的知识构建强大而有效的分类器，已成为当前研究的热点。本文提出了一种基于最大概率投票方案的基于集成的半监督算法。数值结果表明，该算法在分类精度方面优于经典的半监督算法，从而得到了更高效、更稳健的预测模型。
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.