Research on Using Unlabled Text to Improve the Performance of Centroid-based Classification Algorithms
Shunmiao Zhang · Computer Knowledge and Technology · 2007
Centroid-based Classification Algorithms is a high efficient class of Algorithms for Text Categorization.However,in order to obtain classification model well,it requires a number of labeled documents.in practical applications,labeled documents are often very sparse because manually labeling data is tedious and costly,while there are often abundant unlabeled documents.So,we propose OFFUC and ONUC algorithms to mend the matter that centroid-based classification algorithms degrade dramatically when the training data is scarce.Considering that the training data items that are far away from the center of its training category reduce the accuracy of classification.,we exclude them from consideration.Experiment results show that OFFUC and ONUC algorithms,proposed in this paper,can improve the performance of centroid-based Classification Algorithms and outperforms the generic semi-supervised methods when the the number of labeled text is very small.