Unsupervised and supervised learning in cascade for petroleum geology
Expert Systems with Applications: An International Journal
The impact of semi-supervised clustering on text classification
Proceedings of the 17th Panhellenic Conference on Informatics
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This paper shows that the performance of a linear SVM classifier can be improved by utilizing meta-information derived from clustering. Clustering aims in discovering extra knowledge concerning the structure of the whole dataset, (both training and testing set). A co-training algo- rithm is introduced that uses clustering as a complementary step to text classification. At each iteration step of the algo- rithm the clustering phase augments the feature space with a new meta-feature that for each document reflects cluster membership and the classification phase introduces another meta-feature that indicates class membership. Experimen- tal results obtained using widely used datasets demonstrate the effectiveness of the proposed approaches especially for small training sets.