Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An Empirical Study of Feature Selection for Text Categorization based on Term Weightage
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Two-level hierarchical combination method for text classification
Expert Systems with Applications: An International Journal
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Entropy based feature selection for text categorization
Proceedings of the 2011 ACM Symposium on Applied Computing
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Feature selection, an important task in text categorization, is used for the purpose of dimensionality reduction. Feature selection basically can be performed locally and globally. For local selection, distinct feature sets are derived from different classes. The number of feature set is thus depended on the number of class. In contrary, only one universal feature set will be used in global feature selection. It is assumed that the feature set should preserve the characteristic of all classes. Furthermore, feature selection can also be carried out based on relevant feature set only (local dictionary) or both relevant and irrelevant feature set (universal dictionary). In this paper, we explored the different frameworks of feature selection to the task of text categorization on the Reuters(10) and Reuters(115) datasets (variants of Reuters-21578 corpus). We then investigate the efficiency of 7 different local or global feature selections corresponds the use of local and universal dictionary. Our experiments have shown that local feature selection with local dictionary yields optimal categorization results.