An improved support vector machine（ SVM） algorithm is proposed

after analyzing the deficiency of traditional SVM algorithm for imbalanced datasets. It uses adaptive synthetic（ ADASYN） sampling technology for partially resampling on dataset

to increase minority class instances; distribute different weights for different sample point to decrease the influence of noise on training result

cost-sensitive SVM algorithm training is adopted to relieve the bias of hyperplane caused by imbalanced datasets. The proposed algorithm is tested on 6 sets of imbalanced datasets from UCI database. The experimental result shows that the performance of improved SVM algorithm is better than other algorithms and achieve a good balance between minority class accuracy and majority class accuracy.