C4.5: programs for machine learning
C4.5: programs for machine learning
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Document classification by machine: theory and practice
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Email categorization with tournament methods
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
FASiL adaptive email categorization system
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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This paper compares the effectiveness of n-way (n2) classification using a probabilistic classifier to the use of multiple binary probabilistic classifiers. We describe the use of binary classifiers in both Round Robin and Elimination tournaments, and compare both tournament methods and n-way classification when determining the language of origin of speakers (both native and non-native English speakers) speaking English. We conducted hundreds of experiments by varying the number of categories as well as the categories themselves. In all experiments the tournament methods performed better than the n-way classifier, and of these tournament methods, on average, Round Robin performs slightly better than the Elimination tournament.