Building high-performance classifiers using positive and unlabeled examples for text classification

Authors:
Ting Ke;Bing Yang;Ling Zhen;Junyan Tan;Yi Li;Ling Jing
Affiliations:
Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, P.R. China;Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, P.R. China;Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, P.R. China;Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, P.R. China;Department of Mathematics, School of Science, Beijing University of Posts and Telecommunications, Beijing, P.R. China;Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, P.R. China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

Citing 13
Cited 0

Support-Vector Networks

Machine Learning
Learning to classify text from labeled and unlabeled documents

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on 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
Partially Supervised Classification of Text Documents

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PEBL: positive example based learning for Web page classification using SVM

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
One-class svms for document classification

The Journal of Machine Learning Research
Building Text Classifiers Using Positive and Unlabeled Examples

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Text Classification without Negative Examples Revisit

IEEE Transactions on Knowledge and Data Engineering
Learning to Classify Documents with Only a Small Positive Training Set

ECML '07 Proceedings of the 18th European conference on Machine Learning
Learning to classify texts using positive and unlabeled data

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Negative training data can be harmful to text classification

EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper studies the problem of building text classifiers using only positive and unlabeled examples. At present, many techniques for solving this problem were proposed, such as Biased-SVM which is the existing popular method and its classification performance is better than most of two-step techniques. In this paper, an improved iterative classification approach is proposed which is the extension of Biased-SVM. The first iteration of our developed approach is Biased-SVM and the next iterations are to identify confident positive examples from the unlabeled examples. Then an extra penalty factor is given to weight these confident positive examples error. Experiments show that it is effective for text classification and outperforms the Biased-SVM and other two step techniques.