SUCE:基于聚类集成的半监督二分类方法

半监督学习和集成学习是目前机器学习领域中的重要方法。半监督学习利用未标记样本，而集成学习综合多个弱学习器，以提高分类精度。针对名词型数据，本文提出一种融合聚类和集成学习的半监督分类方法SUCE。在不同的参数设置下，采用多个聚类算法生成大量的弱学习器；利用已有的类标签信息，对弱学习器进行评价和选择；通过集成弱学习器对测试集进行预分类，并将置信度高的样本放入训练集；利用扩展的训练集，使用ID3、Nave Bayes、 kNN、C4.5、OneR、Logistic等基础算法对其他样本进行分类。在UCI数据集上的实验结果表明，当训练样本较少时，本方法能稳定提高多数基础算法的准确性。

SUCE: semi-supervised binary classification based on clustering ensemble

Semi-supervised learning and ensemble learning are important methods in the field of machine learning. Semi-supervised learning utilize unlabeled samples, while ensemble learning combines multiple weak learners to improve classification accuracy. This paper proposes a new method called Semi-sUpervised classification through Clustering and Ensemble learning (SUCE) for symbolic data. Under different parameter settings, a number of weak learners are generated using multiple clustering algorithms. Using existing class label information the weak learners are evaluated and selected. The test sets are pre-classified by weak learners ensemble. The samples with high confidence are moved to the training set, and the other samples are classified through the extended training set by using the basic algorithms such as ID3, Nave Bayes, kNN, C4.5, OneR, Logistic and so on. The experimental on the UCI datasets results show that SUCE can steadily improve the accuracy of most of the basic algorithms when there are fewer training samples.