Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
Machine Learning
Evidence Theory and Its Applications
Evidence Theory and Its Applications
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Evaluation of the Information-Theoretic Construction of Multiple Classifier Systems
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Guest Editors' Introduction: Information Enhancement for Data Mining
IEEE Intelligent Systems
Combining Subclassifiers in Text Categorization: A DST-Based Solution and a Case Study
IEEE Transactions on Knowledge and Data Engineering
The combination of multiple classifiers using an evidential reasoning approach
Artificial Intelligence
Efficient Text Classification Using Best Feature Selection and Combination of Methods
Proceedings of the Symposium on Human Interface 2009 on ConferenceUniversal Access in Human-Computer Interaction. Part I: Held as Part of HCI International 2009
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Ontology-Based similarity between text documents on manifold
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
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Experience shows that different text classification methods can give different results. We look here at a way of combining the results of two or more different classification methods using an evidential approach. The specific methods we have been experimenting with in our group include the Support Vector Machine, kNN (nearest neighbors), kNN model-based approach (kNNM), and Rocchio methods, but the analysis and methods apply to any methods. We review these learning methods briefly, and then we describe our method for combining the classifiers. In a previous study, we suggested that the combination could be done using evidential operations [1] and that using only two focal points in the mass functions (see below) gives good results. However, there are conditions under which we should choose to use more focal points. We assess some aspects of this choice from an evidential reasoning perspective and suggest a refinement of the approach.