M. Jayaprakash, D. John Aravindhar, E. R. Naganathan
Abstract: Data mining extracts novel and useful knowledge from large repositories of data and has become an effective analysis and decision means in corporation in many information processing tasks, labels are usually expensive and the unlabeled data points are abundant. To reduce the cost on collecting labels, it is crucial to predict which unlabeled examples are the most informative, i.e., improve the classifier the most if they were labeled. Many active learning techniques have been proposed for text categorization, such as SVM Active and Transductive Experimental Design. However, most of previous approaches try to discover the discriminant structure of the data space, whereas the geometrical structure is not well respected. By minimizing the expected error with respect to the optimal classifier, they can select the most representative and discriminative data points for labeling. Experimental results on text categorization have demonstrated the effectiveness of proposed approach.
Keywords: Text categorization, Novel Active learning, Manifold learning, labeled data