Abstract： To solve the problem that the manual inspection process was badly simulated and the issues were not fully considered in the outlier detection, such as the distribution of the samples surrounding the data to be tested, an outlier detection algorithm called DDSVDD was proposed. In this algorithm, the distance between the test samples and the target samples and the distribution information of the test sample region were all considered. The distance and the average density were taken into account to determine the type of data tested near the decisionmaking boundary in highdimensional feature space. In the training stage, the distribution of the target training samples near the decisionmaking boundary was considered, and part of the target samples near the concentrated boundary of training sample were set aside, whose average densities were calculated. In the forecasting stage, the attribution of test samples was estimated by using distance and average density synthetically. The algorithmic derivation was carried out, and the codes of training stage and checking stage were given. The experiment based on UCI data was done. The results show that DDSVDD is effective, and the recognition rate is high.
2011, Vol. 32 
