As a supervised classifier

Support Vector Machine (SVM) has prominent advantages in solving some problems on petty and nonlinear datasets

but it is unsatisfying in tackling with imbalanced datasets. Random under-sampling has been a widely used method to improve SVM's performance on imbalanced data

but its stability is easily influenced by the nature of randomness. A modified SVM based on under-sampling method is presented to classify imbalanced data. Compared with the random under-sampling technique

it is shown through experiments on natural datasets that the new proposed under-sampling method is more stable in classifying imbalanced data

and exhibits improved SVM performance in classifying imbalanced data for many cases.