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.