Candidate Vectors Selection for Training Support Vector Machines

In this paper, a novel and concise method for the selection of candidate vectors (SCV) is proposed based on the structural information of two classes in the input space. First, the Euclidean distance of all samples to the boundary of the other classes is calculated. Then the relative distance is computed to reorder training samples ascendingly, and boundary samples will rank in front of others and have a higher probability to be candidate support vectors. A certain proportion of the foremost ranked samples are selected to form examples subset for training the SVM classification function by using the SMO. For linearly non-separable datasets with noise, an abnormal examples filtering (AEF) procedure is designed to find abnormal examples or outliers that may give rise to the distortion of structural information on the boundaries of two classes. Finally, two datasets are used to test the prediction accuracy of the SVM decision function estimated by the SMO and the AEF+SCV+SMO.