Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Classifying news stories using memory based reasoning
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
SIGIR '94 Proceedings of the 17th 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
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European 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 Discriminant Analysis for Multi-class Classification
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Virtual relevant documents in text categorization with support vector machines
Information Processing and Management: an International Journal
Query-biased summarization considering difference of paragraphs
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
A term weighting approach for text categorization
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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To improve performance in text categorization, it is important to extract distinctive features for each class. This paper proposes topic difference factor analysis (TDFA) as a method to extract projection axes that reflect topic differences between two document sets. Suppose all sentence vectors that compose each document are projected onto projection axes. TDFA obtains the axes that maximize the ratio between the document sets as to the sum of squared projections by solving a generalized eigenvalue problem. The axes are called topic difference factors (TDF's). By applying TDFA to the document set that belongs to a given class and a set of documents that is misclassified as belonging to that class by an existent classifier, we can obtain features that take large values in the given class but small ones in other classes, as well as features that take large values in other classes but small ones in the given class. A classifier was constructed applying the above features to complement the kNN classifier. As the results, the micro averaged F1 measure for Reuters-21578 improved from 83.69 to 87.27%.