Affiliation:
1. School of Mathematics and Statistics, Northeast Normal University, Changchun, China
2. School of Information Science and Technology, Northeast Normal University, Changchun, China
Abstract
<abstract>
<p>There are two main factors involved in documents classification, document representation method and classification algorithm. In this study, we focus on document representation method and demonstrate that the choice of representation methods has impacts on quality of classification results. We propose a document representation strategy for supervised text classification named document representation based on global policy (<italic>DRGP</italic>), which can obtain an appropriate document representation according to the distribution of terms. The main idea of <italic>DRGP</italic> is to construct the optimization function through the importance of terms to different categories. In the experiments, we investigate the effects of <italic>DRGP</italic> on the 20 Newsgroups, Reuters21578 datasets, and using the <italic>SVM</italic> as classifier. The results show that the <italic>DRGP</italic> outperforms other text representation strategy schemes, such as Document Max, Document Two Max and global policy.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference33 articles.
1. M. Lan, S. Sung, H. Low, C. Tan, A comparative study on term weighting schemes for text categorization, in Proceedings 2005 IEEE International Joint Conference on Neural Networks, 1 (2005), 546–551. https://doi.org/10.1109/IJCNN.2005.1555890
2. X. Li, A. Zhang, C. Li, J. Ouyang, Y. Cai, Exploring coherent topics by topic modeling with term weighting, Inf. Process. Manage., 54 (2018), 1345–1358. https://doi.org/10.1016/j.ipm.2018.05.009
3. M. Lan, C. Tan, J. Su, Y. Lu, Supervised and traditional term weighting methods for automatic text categorization, IEEE Trans. Pattern Anal. Mach. Intell., 31 (2008), 721–735. https://doi.org/10.1109/TPAMI.2008.110
4. E. H. Han, G. Karypis, V. Kumar, Text Categorization Using Weight Adjusted K-Nearest Neighbor Classification, Proc. Pacific Asia Conf. Knowl. Discovery Data Min., (2001), 53–65. https://doi.org/10.1007/3-540-45357-1_9
5. X. Quan, W. Liu, B. Qiu, Term weighting schemes for question categorization, IEEE Trans. Pattern Anal. Mach. Intell., 33 (2010), 1009–1021. https://doi.org/10.1109/TPAMI.2010.154
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