Feature selection in text classification via SVM and LSI

Authors:
Ziqiang Wang;Dexian Zhang
Affiliations:
School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, China;School of Information Science and Engineering, Henan University of Technology, Zheng Zhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 10
Cited 1

A re-examination of text categorization methods

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic indexing: a probabilistic analysis

Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
A statistical learning learning model of text classification for support vector machines

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization

ACM Computing Surveys (CSUR)
Introduction to Modern Information Retrieval

Introduction to Modern Information Retrieval
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
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
Efficient multi-way text categorization via generalized discriminant analysis

CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Support vector machines for spam categorization

IEEE Transactions on Neural Networks

Text classification with the support of pruned dependency patterns

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Text classification is a problem of assigning a document into one or more predefined classes. One of the most interesting issues in text categorization is feature selection. This paper proposes a novel approach in feature selection based on support vector machine(SVM) and latent semantic indexing(LSI), which can identify LSI-subspace that is suited for classification. Experimental results show that the proposed method can achieve higher classification accuracies and is of less training and prediction time.