Using a generalized instance set for automatic text categorization
Proceedings of the 21st 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
Centroid-Based Document Classification: Analysis and Experimental Results
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Email classification with co-training
CASCON '01 Proceedings of the 2001 conference of the Centre for Advanced Studies on Collaborative research
Robustness of regularized linear classification methods in text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Searching for ground truth: a stepping stone in automating genre classification
DELOS'07 Proceedings of the 1st international conference on Digital libraries: research and development
Genre classification in automated ingest and appraisal metadata
ECDL'06 Proceedings of the 10th European conference on Research and Advanced Technology for Digital Libraries
Progress in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Building a document genre corpus: a profile of the KRYS I corpus
IRSG'08 Proceedings of the 2008 BCS-IRSG conference on Corpus Profiling
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Improving the accuracy of assigning new email messages to small folders can reduce the likelihood of users creating duplicate folders for some topics. In this paper we presented a hybrid classification model, PERC, and use the Enron Email Corpus to investigate the performance of kNN, SVM and PERC in a simulation of a real-time situation. Our results show that PERC is significantly better at assigning messages to small folders. The effects of different parameter settings for the classifiers are discussed.