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A Safe Semi-supervised Classification Algorithm Using Multiple Classifiers Ensemble

机译：使用多分类器集合的安全半监督分类算法

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摘要

In order to improve the performance of semi-supervised learning, a safe semi-supervised classification algorithm using multiple classifiers ensemble (S3C-MC) is proposed. First, unlabeled samples are filtered and unlabeled samples with small ambiguity are selected for semi-supervised learning. Next, the labeled training set is sampled to multiple subsets and they generate multiple classifiers to predict the filtered unlabeled sample respectively. The predicted label is formed by multiple classifiers with weighted voting mechanism, and the weight of classifier is changing constantly according to the correctness of the prediction for unlabeled samples by classifier. Then, security verification is carried out to ensure that the classifier evolves in the direction of error reduction when the new sample is added. Only the label making classifiers error lower and having the same predictive value with the three classifiers in security verification is added into the labeled set to expand the number of labeled sets. Finally, the algorithm iterates until the unlabeled sample set is empty. The experiment is carried out on the UCI data set and the result shows that the proposed S3C-MC has good safety and has a higher classification rate.
机译：为了提高半监督学习的性能，提出了一种使用多个分类器（S3C-MC）的安全半监督分类算法（S3C-MC）。首先，将滤除未标记的样本，并选择具有小模糊性的未标记样品进行半监督学习。接下来，将标记的训练集采样到多个子集，并且它们生成多个分类器以分别预测滤波的未标记样本。预测标签由具有加权投票机制的多个分类器形成，并且分类器的重量根据分类器对未标记样本的预测的正确性不断变化。然后，执行安全验证以确保分类器在添加新样本时的误差方向上演变。只添加了在安全验证中具有三分类器的标签误差和具有与三分类器相同的预测值，以扩展标记集的数量。最后，算法迭代，直到未标记的样本集是空的。实验在UCI数据集上进行，结果表明，所提出的S3C-MC具有良好的安全性并具有更高的分类率。

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