代价敏感的半监督Laplacian支持向量机

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 Semi-Supervised Laplacian Support Vector Machine

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.