Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Advances in Neural Information Processing Systems 5, [NIPS Conference]
PAC Learning from Positive Statistical Queries
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
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
Semi-supervised learning with explicit misclassification modeling
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Most existing text classification work assumes that training data are completely labeled. In real life, some information retrieval problems can only be described as learning a binary classifier from a set of incompletely labeled examples, where a small set of labeled positive examples and a very large set of unlabeled ones are provided. In this case, all of the traditional text classification methods can't work properly. In this paper, we propose a method called Weighted Voting Classifier, which is an improved 1-DNF algorithm. Experimental results on the Reuters-21578 set show that our algorithm Weighting Voting Classifier outperforms PEBL and one-class SVM in terms of F measure. Weighting Voting Classifier can achieve high F score when comparing with PEBL and one-class SVM. Furthermore, the reduction of iterations is 2.26 when comparing the method of PEBL with ours.