Aggressive dimensionality reduction with reinforcement local feature selection for text categorization

Authors:
Wenbin Zheng;Yuntao Qian
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, China;College of Computer Science, Zhejiang University, Hangzhou, China
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

Citing 9
Cited 1

Feature selection, perceptron learning, and a usability case study for text categorization

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization

ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5

ICML '04 Proceedings of the twenty-first international conference on Machine learning
A concept-based model for enhancing text categorization

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
One-against-one fuzzy support vector machine classifier: An approach to text categorization

Expert Systems with Applications: An International Journal
An examination of feature selection frameworks in text categorization

AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Text classification using small number of features

MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition

Dimensionality reduction with category information fusion and non-negative matrix factorization for text categorization

AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

A major problem of text categorization is the high dimensionality of the input feature space. This paper proposes a novel approach for aggressive dimensionality reduction in text categorization. This method utilizes the local feature selection to obtain more positive terms and then scales the weighting in the global level to suit the classifier. After that the weighting is enhanced with the feature selection measure to improve the distinguishing capability. The validity of this method is tested on two benchmark corpuses by the SVM classifier with four standard feature selection measures.