关键词:单值分类; 支持向量数据描述; K-means聚类; 局部疏密度
New classification algorithm K-means clustering combined with SVDD
LIU Yan-hong, XUE An-rong, SHI Xi-yun
(School of Computer Science & Telecommunications Engineering, Jiangsu University, Zhenjiang Jiangsu 212013, China)
Abstract:This paper proposed an improved SVDD algorithm by introducing a local density degree for each data point in order to improve the support vector data description(SVDD) classification accuracy. Proved to improve the classification accuracy, but the increase of computational complexity. To this end, first divided the whole data set into k clusters using K-means clustering algorithm. Then, trained the k clusters in parallel by improved SVDD. Finally, trained the k obtained local support vector sets and got the final overall decision border. As a result of divide and conquer method and parallel computing, improved the efficiency of the algorithm. Synthetic data and real data experimental results show that the proposed method than SVDD algorithm, training time is reduced to 10% and classification error rate lower than the original by almost half. Therefore, the proposed algorithm improves the classification accuracy and algorithm efficiency. ......