Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Knowledge discovery in databases: an overview
AI Magazine
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
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
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Data extraction as text categorization: an experiment with the MUC-3 corpus
MUC3 '91 Proceedings of the 3rd conference on Message understanding
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
High performance text document clustering
High performance text document clustering
A Survey of Semi-Supervised Learning Methods
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
A multi-view approach to semi-supervised document classification with incremental Naive Bayes
Computers & Mathematics with Applications
A novel hybrid ACO-GA algorithm for text feature selection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Inductive Inference for Large Scale Text Classification: Kernel Approaches and Techniques
Inductive Inference for Large Scale Text Classification: Kernel Approaches and Techniques
Rough set and ensemble learning based semi-supervised algorithm for text classification
Expert Systems with Applications: An International Journal
Some contributions to semi-supervised learning
Some contributions to semi-supervised learning
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Text categorization is one of the fundamental tasks in text mining. Classical supervised methods need lot of labeled data to train a classifier. Since assigning labels to the large amount of data is very costly and time consuming, it is useful to use data sets without labels. So many different semi-supervised learning methods have been studied recently. Among these semi-supervised methods, self-training is one of the important learning algorithms that classifies unlabeled samples with small amount of labeled ones and adds the most confident samples to the training set. In this paper, dynamic weighting beside majority vote approach is applied to classify the unlabeled data to reliable and unreliable classes. Then, the reliable data are added to the training set and the remaining data including unreliable data are classified in iterative process. We tested this method on the extracted features of ten common Reuter-21578 classes. Experimental result indicates that proposed method improves the classification performance and it's effective.