局部几何保持的Laplacian代价敏感支持向量机

不平衡数据广泛存在于现实生活中,代价敏感学习能有效解决这一问题。然而,当数据的标记信息有限或不足时,代价敏感学习分类器的分类精度大大下降,分类性能得不到保证。针对这一情况,该文提出了一种局部几何保持的Laplacian代价敏感支持向量机(LPCS-LapSVM),该模型基于半监督学习框架,将代价敏感学习和类内局部保持散度的思想引入其中,从考虑内在可分辨信息和样本的局部几何分布两方面来提高代价敏感支持向量机在标记信息有限的场景中的分类性能。UCI数据集上的实验结果表明了该算法的有效性。

Locality Preserving Cost Sensitive Laplacian Support Vector Machine

Cost-sensitive learning can efficiently solve the class imbalanced problem in practical applications. However, when the label information of samples is limited or insufficient, the classification accuracy of the cost sensitive learning classifier is significantly reduced. To address this issue, a novel classification method named locality preserving cost sensitive Laplacian support vector machine (LPCS-LapSVM) is proposed. LPCS-LapSVM extends the semi supervised learning framework by introducing the ideas of cost-sensitive learning and local geometry of data together. Due to considering the intrinsic information and the local geometric distribution of samples, LPCS-LapSVM improves the classification performance of cost sensitive support vector machine in the classification scene with the limited labeled samples. Experimental results on UCI data set demonstrate the advantages as well as the superiority of the proposed method.