Class-driven correlation learning for chinese document categorization using discriminative features

Authors:
Xian Wu;Lingli Zhou;Xiang Li;Jianhuang Lai
Affiliations:
Sun Yat-sen University, and Nanfang Media Group, Guangzhou, China;Sun Yat-sen University;University of Electronic Science and Technology of China;Sun Yat-sen University
Venue:
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Year:
2011

Citing 7
Cited 0

Meaningful term extraction and discriminative term selection in text categorization via unknown-word methodology

ACM Transactions on Asian Language Information Processing (TALIP)
Feature selection with conditional mutual information maximin in text categorization

Proceedings of the thirteenth ACM international conference on Information and knowledge management
A novel refinement approach for text categorization

Proceedings of the 14th ACM international conference on Information and knowledge management
An Effective Dimension Reduction Approach to Chinese Document Classification Using Genetic Algorithm

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Hierarchical Text Categorization Based on Multiple Feature Selection and Fusion of Multiple Classifiers Approaches

FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Information gain and divergence-based feature selection for machine learning-based text categorization

Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Feature selection on Chinese text classification using character n-grams

RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology

Quantified Score

Hi-index	0.00

Visualization

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.