Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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 Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Improving Multiclass Pattern Recognition by the Combination of Two Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Text Categorization Technique Using Distributional Clustering and Learning Logic
IEEE Transactions on Knowledge and Data Engineering
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
Text classification: a sequential reading approach
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Reducing number of classifiers in DAGSVM based on class similarity
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
A novel probabilistic feature selection method for text classification
Knowledge-Based Systems
Projected-prototype based classifier for text categorization
Knowledge-Based Systems
The impact of preprocessing on text classification
Information Processing and Management: an International Journal
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Multiclass support vector machine (SVM) methods are well studied in recent literature. Comparison studies on UCI/statlog multiclass datasets suggest using one-against-one method for multiclass SVM classification. However, in unilabel (multiclass) text categorization with SVMs, no comparison studies exist with one-against-one and other methods, e.g. one-against-all and several well-known improvements to these approaches. In this paper, we bridge this gap by performing empirical comparison of standard one-against-all and one-against-one, together with three improvements to these standard approaches for unilabel text categorization with SVM as base binary learner. We performed all our experiments on three standard text corpuses using two types of document representation. Outcome of our experiments partly support Rifkin and Klautau's (2004) statement that, for small scale unilabel text categorization tasks, if parameters of the classifiers are well tuned, one-against-all will have better performance than one-against-one and other methods.