Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Text databases & document management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Feature Subset Selection in Text-Learning
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
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Using typical testors for feature selection in text categorization
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
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A major characteristic of text document categorization problems is the extremely high dimensionality of text data. In this paper we explore the usability of the Oscillating Search algorithm for feature/word selection in text categorization. We propose to use the multiclass Bhattacharyya distance for multinomial model as the global feature subset selection criterion for reducing the dimensionality of the bag of words vector document representation. This criterion takes into consideration inter-feature relationships. We experimentally compare three subset selection procedures: the commonly used best individual feature selection based on information gain, the same based on individual Bhattacharyya distance, and the Oscillating Search to maximize Bhattacharyya distance on groups of features. The obtained feature subsets are then tested on the standard Reuters data with two classifiers: the multinomial Bayes and the linear SVM. The presented experimental results illustrate that using a non-trivial feature selection algorithm is not only computationally feasible, but it also brings substantial improvement in classification accuracy over traditional, individual feature evaluation based methods.