The nature of statistical learning theory
The nature of statistical learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Pairwise classification and support vector machines
Advances in kernel methods
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
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The VLDB Journal — The International Journal on Very Large Data Bases
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Efficient multi-way text categorization via generalized discriminant analysis
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Using hypothesis margin to boost centroid text classifier
Proceedings of the 2007 ACM symposium on Applied computing
Hierarchical document classification using automatically generated hierarchy
Journal of Intelligent Information Systems
Text categorization via generalized discriminant analysis
Information Processing and Management: an International Journal
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Enhancing the Performance of Centroid Classifier by ECOC and Model Refinement
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Automatic text categorization based on content analysis with cognitive situation models
Information Sciences: an International Journal
A comparative study of decision tree approaches to multi-class Support Vector Machines
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
A global-ranking local feature selection method for text categorization
Expert Systems with Applications: An International Journal
Semi-supervised learning for automatic conceptual property extraction
CMCL '12 Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics
Assessing the quality of textual features in social media
Information Processing and Management: an International Journal
Class-indexing-based term weighting for automatic text classification
Information Sciences: an International Journal
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Support vector machines (SVMs) excel at two-class discriminative learning problems. They often outperform generative classifiers, especially those that use inaccurate generative models, such as the naïve Bayes (NB) classifier. On the other hand, generative classifiers have no trouble in handling an arbitrary number of classes efficiently, and NB classifiers train much faster than SVMs owing to their extreme simplicity. In contrast, SVMs handle multi-class problems by learning redundant yes/no (one-vs-others) classifiers for each class, further worsening the performance gap. We propose a new technique for multi-way classification which exploits the accuracy of SVMs and the speed of NB classifiers. We first use a NB classifier to quickly compute a confusion matrix, which is used to reduce the number and complexity of the two-class SVMs that are built in the second stage. During testing, we first get the prediction of a NB classifier and use that to selectively apply only a subset of the two-class SVMs. On standard benchmarks, our algorithm is 3 to 6 times faster than SVMs and yet matches or even exceeds their accuracy.