代价敏感学习是机器学习领域的一个研究热点.在实际应用中,数据集往往是不平衡的,存在着大量的无标签样本,只有少量的有标签样本,并且存在噪声.虽然针对该情况的代价敏感学习方法的研究已取得了一定的进展,但还需要进一步的深入研究.为此,本文提出了一种基于代价敏感的半监督Laplacian支持向量机.该模型在采用无标签扩展策略的基础上,将考虑了数据不平衡的错分代价融入到Laplacian支持向量机的经验损失和Laplacian正则化项中.考虑到噪声样本对决策平面的影响,本文定义了一种样本依赖的代价,对噪声样本赋予较低的权重.在7个UCI数据集和8个NASA软件数据集上的实验结果表明了本文算法的有效性.%Cost sensitive learning is the hot research area in machine learning. In practical real applications, the datasets are usually class-imbalanced,most of the samples are unlabeled,only a few of the samples are labeled, and noise samples are existed. Although some progress has been made in cost sensitive learning for such situation, it needs further solved. For that we propose a semi-supervised Laplacian support vector machine based on cost sensitive learning. On the basis of label propagation, the proposed model integrates the misclassification costs considering class-imbalance into the hinge loss and Laplacian regularization of the Laplacian support vector machine. At the same time, considering the effect on the decision hypersphere of the noise samples, we define an example-dependent cost which makes the weights of noise samples lower. The experimental results on 7 UCI, 8 NASA datasets demonstrate the superiority of our proposed algorithm.
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