Nesting support vector machinte for muti-classification [machinte read machine]

Support vector machines (SVMS) were originally designed for binary classifications. As for multi-classifications, they are usually converted into binary ones, where unclassifiable regions usually exist. To overcome this drawback, a novel method called nesting support vector machine (NSVMS) for multi-classification is presented in this paper. Our ideas are as follows: firstly, construct the optimal hyperplanes based on one-against-one algorithm. Secondly, if there exist data points in the middle unclassifiable region, select them to construct optimal hyperplanes with the same hyperparameters. Thirdly, repeat the second step until there are no data points in the unclassifiable regions or the regions disappear. In order to examine the training accuracy and the generalization performance of the proposed algorithm, one-against-one algorithm, fuzzy least square support vector machine (FLS-SVM) and the proposed algorithm are applied to two UCI datasets. The results show that the training accuracy of the proposed algorithm is higher than the others, and its generalization performance is also comparable with them.