Title | Efficient text classification by weighted proximal SVM |
Authors | Zhuang, Dong Zhang, Benyu Yang, Qiang Yan, Jim Chen, Zheng Chen, Ying |
Affiliation | Computer Science and Engineering, Beijing Institute of Technology, Beijing 100081, China Microsoft Research Asia, Beijing 100080, China Computer Science, Hong Kong University of Science and Technology, Hong Kong, Hong Kong Department of Information Science, School of Mathematical Science, Peking University |
Issue Date | 2005 |
Citation | 5th IEEE International Conference on Data Mining, ICDM 2005.Houston, TX, United states. |
Abstract | 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 [3]. 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 RCVl-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. ? 2005 IEEE. |
URI | http://hdl.handle.net/20.500.11897/328411 |
DOI | 10.1109/ICDM.2005.56 |
Indexed | EI |
Appears in Collections: | 数学科学学院 |