Abstract
In view of the two problems of the SVM algorithm in processing large data, the paper proposed a weighted Euclidean distance, radial integral kernel function SVM and dimensionality reduction algorithm for large data packet classification. The SVM cannot handle multi classification and time of building model is long. The algorithm solved these problems. The improved algorithm reconstructs the data feature space, makes the boundary of different data samples clearer, shortens the modeling time, and improves the accuracy of classification. The proposed method verified the feasibility and effectiveness with experiments. The experimental results show that the improved algorithm can achieve better results when multi-duplicated samples and large data capacity are used for multi classification.
