Representation and learning in information retrieval
Representation and learning in information retrieval
SIGIR '94 Proceedings of the 17th 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
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
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
A Feature Selection Framework for Text Filtering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using Intuitionistic Fuzzy Sets in Text Categorization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Automated Classification and Categorization of Mathematical Knowledge
Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
A Hybrid Statistical Data Pre-processing Approach for Language-Independent Text Classification
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Computers in Biology and Medicine
Entropy based feature selection for text categorization
Proceedings of the 2011 ACM Symposium on Applied Computing
Feature selection strategy in text classification
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
An effective feature selection method for text categorization
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Feature sub-set selection metrics for Arabic text classification
Pattern Recognition Letters
A new nearest neighbor rule for text categorization
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Enhancement of DTP feature selection method for text categorization
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Categorical proportional difference: a feature selection method for text categorization
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Comparison of text feature selection policies and using an adaptive framework
Expert Systems with Applications: An International Journal
Interactive text document clustering using feature labeling
Proceedings of the 2013 ACM symposium on Document engineering
Improving Text Classification Accuracy by Training Label Cleaning
ACM Transactions on Information Systems (TOIS)
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We tackle two different problems of text categorization (TC), namely feature selection and classifier induction. Feature selection (FS) refers to the activity of selecting, from the set of r distinct features (i.e. words) occurring in the collection, the subset of r′ ≪ r features that are most useful for compactly representing the meaning of the documents. We propose a novel FS technique, based on a simplified variant of the X2 statistics. Classifier induction refers instead to the problem of automatically building a text classifier by learning from a set of documents pre-classified under the categories of interest. We propose a novel variant, based on the exploitation of negative evidence, of the well-known k-NN method. We report the results of systematic experimentation of these two methods performed on the standard REUTERS-21578 benchmark.