Fifth IEEE International Conference on Data Mining (ICDM'05) (2005)
Nov. 27, 2005 to Nov. 30, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.56
Dong Zhuang , Beijing Institute of Technology
Benyu Zhang , Microsoft Research Asia
Qiang Yang , Hong Kong University of Science and Technology
Jun Yan , Peking University
Zheng Chen , Microsoft Research Asia
Ying Chen , Beijing Institute of Technology
In this paper, we present an algorithm that can classify large-scale text data with high classification quality and fast training speed. Our method is based on a novel extension of the proximal SVM mode . Previous studies on proximal SVM have focused on classification for low dimensional data and did not consider the unbalanced data cases. Such methods will meet difficulties when classifying unbalanced and high dimensional data sets such as text documents. In this work, we extend the original proximal SVM by learning a weight for each training error. We show that the classification algorithm based on this model is capable of handling high dimensional and unbalanced data. In the experiments, we compare our method with the original proximal SVM (as a special case of our algorithm) and the standard SVM (such as SVM light) on the recently published RCV1-v2 dataset. The results show that our proposed method had comparable classification quality with the standard SVM. At the same time, both the time and memory consumption of our method are less than that of the standard SVM.
D. Zhuang, J. Yan, Z. Chen, B. Zhang, Y. Chen and Q. Yang, "Efficient Text Classification by Weighted Proximal SVM," Fifth IEEE International Conference on Data Mining (ICDM'05)(ICDM), Houston, Texas, 2005, pp. 538-545.