In order to improve the conventional support vector domain description (C-SVDD) algorithm's classification performance under unbalanced datasets

a novel maximum soft margin support vector domain descriptionalgorithm based on density sensitivity (DSMSM-SVDD) is presented. The relative density informationof the majority samples is introduced to reflect the impact of original training sample's space distribution on the optimal interface

by adding the maximum soft margin regularization term in the objective function

the classification boundary of the C-SVDD algorithm is shifted tominority classes

and consequently the classification performance of the proposed algorithm is significantly improved. Firstly

the relative density of each majority sample is calculated to reflect the importance of the training samples

and then the obtained training samples with relative density are input into the proposed DSMSM-SVDD algorithm to implement the classificationtask. In the experiments

the relationship of the parameters and the influence of the parameters on classification performance are investigated. Finally

the comparison results with C-SVDD algorithm demonstrate that the proposed algorithm is superior to the C-SVDD algorithm in the case of unbalanced data.