Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Information Retrieval
Bayesian online classifiers for text classification and filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Centroid-Based Document Classification: Analysis and Experimental Results
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
A refinement approach to handling model misfit in text categorization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling multi-class support vector machines using inter-class confusion
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A novel refinement approach for text categorization
Proceedings of the 14th ACM international conference on Information and knowledge management
Neighbor-weighted K-nearest neighbor for unbalanced text corpus
Expert Systems with Applications: An International Journal
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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With the aim of improving the performance of centroid text classifier, we attempt to make use of the advantages of Error-Correcting Output Codes (ECOC) strategy. The framework is to decompose one multi-class problem into multiple binary problems and then learn the individual binary classification problems by centroid classifier. However, this kind of decomposition incurs considerable bias for centroid classifier, which results in noticeable degradation of performance for centroid classifier. In order to address this issue, we use Model-Refinement strategy to adjust this so-called bias. The basic idea is to take advantage of misclassified examples in the training data to iteratively refine and adjust the centroids of text data. The experimental results reveal that Model-Refinement strategy can dramatically decrease the bias introduced by ECOC, and the combined classifier is comparable to or even better than SVM classifier in performance.