Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Boosting to correct inductive bias in text classification
Proceedings of the eleventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A refinement approach to handling model misfit in text categorization
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
Adapting centroid classifier for document categorization
Expert Systems with Applications: An International Journal
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Centroid Classifier has been shown to be a simple and yet effective method for text categorization. However, it is often plagued with model misfit (or inductive bias) incurred by its assumption. To address this issue, a novel Model Adjustment algorithm was proposed. The basic idea is to make use of some criteria to adjust Centroid Classifier model. In this work, the criteria include training-set errors as well as training-set margins. The empirical assessment indicates that proposed method performs slightly better than SVM classifier in prediction accuracy, as well as beats it in running time.