Research on Large Scale Data Sets Categorization Based on SVM

Support Vector Machines algorithms are not appropriate for the large data sets because of high training complexity. To address this issue, this paper presents a two stage SVM classification algorithm based on fuzzy clustering. The algorithm is divided into two phases. In the first phase, an approximate decision hyper-plane is obtained by weighted SVM which using the data after the fuzzy clustering as training data sets. In the second phase, the decision hyper-plane is obtained by SVM using the data near to the approximate hyper-plane obtained in the first phase. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than the standard SVM. The improved approach has a distinctive advantage on dealing with huge data sets.