Representation and learning in information retrieval
Representation and learning in information retrieval
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
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
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Feature selection is one of the most interesting problems in machine learning in general and text categorization in particular. Previous researches in feature selection often focus on choosing appropriate measument to evaluate features. This seems to be good for structured data but rather difficult to text, a nonstructured data. Our main contribution in this paper is to propose a new approach of feature selection based on multi-criteria ranking of features. A new model for feature selection is propose; based on a threshold value for each criterion, a new procedure for feature selection is proposed and applied to a text categorization. Experiments show that the proposed model outperforms performances in compare to conventional feature selection methods.