A New Nearest Neighbor Rule for Text Categorization

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.