Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th 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
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
High-performing feature selection for text classification
Proceedings of the eleventh international conference on Information and knowledge management
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
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
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Oscillating feature subset search algorithm for text categorization
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Parallel nearest neighbour algorithms for text categorization
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
BR: A New Method for Computing All Typical Testors
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A fast implementation of the CT_EXT algorithm for the testor property identification
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Typical testors generation based on an evolutionary algorithm
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
| Hi-index | 0.00 |
A major difficulty of text categorization problems is the high dimensionality of the feature space. Thus, feature selection is often performed in order to increase both the efficiency and effectiveness of the classification. In this paper, we propose a feature selection method based on Testor Theory. This criterion takes into account inter-feature relationships. We experimentally compared our method with the widely used information gain using two well-known classification algorithms: k-nearest neighbour and Support Vector Machine. Two benchmark text collections were chosen as the testbeds: Reuters- 21578 and Reuters Corpus Version 1 (RCV1-v2). We found that our method consistently outperformed information gain for both classifiers and both data collections, especially when aggressive feature selection is carried out.