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
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
On the use of neighbourhood-based non-parametric classifiers
Pattern Recognition Letters - special issue on pattern recognition in practice V
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization
ECDL '00 Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A New Classification Rule based on Nearest Neighbour Search
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
Improving kNN text categorization by removing outliers from training set
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Using the αβ-Neighborhood for Adaptive Document Filtering
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: 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
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The nearest neighbor (NN) rule is usually chosen in a large number of pattern recognition systems due to its simplicity and good properties. In particular, this rule has been successfully applied to text categorization. A vast number of NN algorithms have been developed during the last years. They differ in how they find the nearest neighbors, how they obtain the votes of categories, and which decision rule they use. A new NN classification rule which comes from the use of a different definition of neighborhood is introduced in this paper. The experimental results on Reuters-21578 standard benchmark collection show that our algorithm achieves better classification rates than the k-NN rule while decreasing classification time.