Class-driven correlation learning for chinese document categorization using discriminative features
Authors: 
Xian Wu, Lingli Zhou, Xiang Li, Jianhuang LaiAuthors Info & Claims
Pages 142 - 145
Abstract
This paper proposes a class-driven correlation learning method for Chinese document categorization to assign one suitable category in the first level to a document. Discriminative features are selected from candidate terms with high occurrence probability in each category. Class-driven correlation learning is then performed to produce a set of projections and further construct a code matrix to record the correlations between different classes of documents. A new document is classified by implementing the decision rule through the results from class-driven correlation learning. The competitive results from the experiments performed on TanCorp corpus indicate the superiority of the proposed method.
References
[1]
S. Tan, X. Cheng, M. M. Ghanem, B. Wang and H. Xu, A novel refinement approach for text categorization, ACM International Conference on Information and Knowledge Management, 2005.
[2]
G. Wang and F. H. Lochovsky, Feature selection with conditional mutual information maximin in text categorization. ACM International Conference on Information and Knowledge Management, 2004.
[3]
C. Lee and G. C. Lee, Information gain and divergence-based feature selection for machine learning-based text categorization. Information Processing and Management, 42(1):155--165, 2006.
[4]
M. A. Mesleh, Chi square feature extraction based SVMs Arabic language text categorization system. Journal of Computer Science, 3(6):430--435, 2007.
[5]
Y. S. Lai and C. H. Wu, Meaningful term extraction and discriminative term selection in text categorization via unknown word methodology. ACM Trasactions on Asian Language Information Processing, 1(1):34--64, 2002.
[6]
Z. Wei, D. Miao, J. H. Chauchat and C. Zhong, Feature selection on Chinese text classification using character N-grams. Lecture Notes in Computer Science, 5552/2009:500--507, 2008.
[7]
Z. Guo, L. Lu, S. Xi and F. Sun, An effective dimension reduction approach to Chinese document classification using genetic algorithm. Lecture Notes in Computer Science, 5552/2009:480--489, 2008.
[8]
M. Y. Jia, D. Q. Zheng, B. R. Yang and Q. X. Chen, Hierarchical text catetorization based on multiple feature selection and fusion of multiple classifiers approaches. International Conference on Fuzzy Systems and Knowledge Discovery, 2009.
[9]
X. Cheng, S. Tan and L. Tang, Using dragpusing to refine concept index for text categorization. Journal of Computer Science and Technology, 21(4):592--596, 2006.
Index Terms
- Class-driven correlation learning for chinese document categorization using discriminative features
Recommendations
Generative Multi-Label Correlation Learning
In real-world applications, a single instance could have more than one label. To solve this task, multi-label learning methods emerged in recent years. It is a more challenging problem for many reasons, such as complex label correlation, long-tail label ...
Multi-label classification with weak labels by learning label correlation and label regularization
AbstractIn conventional multi-label learning, each training instance is associated with multiple available labels. Nevertheless, real-world objects usually exhibit more sophisticated properties such as abundant irrelevant features, incomplete labels, ...
Research on Medical Document Categorization
FBIE '08: Proceedings of the 2008 International Seminar on Future BioMedical Information EngineeringMedical document categorization is the process of automatically assigning one or more predefined category labels to medical documents. Document indexing plays a very important role in the process of classification. This paper proposes an improved method ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
August 2011
208 pages
ISBN:9781450309189
DOI:10.1145/2043674
- General Chairs:
- Changsheng Xu,
- Ralf Steinmetz,
- Program Chairs:
- Abdulmotaleb El Saddik,
- Chong-Wah Ngo,
- Xiao Wu
Copyright © 2011 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
- Sichuan University
- Chinese Academy of Sciences
- SCF: Sichuan Computer Federation
- Southwest Jiaotong University
- Beijing ACM SIGMM Chapter
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 August 2011
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
ICIMCS '11
Sponsor:
- SCF
ICIMCS '11: The Third International Conference on Internet Multimedia Computing and Service
August 5 - 7, 2011
Chengdu, China
Acceptance Rates
Overall Acceptance Rate 163 of 456 submissions, 36%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 78Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)1
Reflects downloads up to 23 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in