A Similarity Based Supervised Decision Rule for Qualitative Improvement of Text Categorization

The similarity based decision rule computes the similarity between a new test document and the existing documents of the training set that belong to various categories. The new document is grouped to a particular category in which it has maximum number of similar documents. A document similarity based supervised decision rule for text categorization is proposed in this article. The similarity measure determine the similarity between two documents by finding their distances with all the documents of training set and it can explicitly identify two dissimilar documents. The decision rule assigns a test document to the best one among the competing categories, if the best category beats the next competing category by a previously fixed margin. Thus the proposed rule enhances the certainty of the decision. The salient feature of the decision rule is that, it never assigns a document arbitrarily to a category when the decision is not so certain. The performance of the proposed decision rule for text categorization is compared with some well known classification techniques e.g., k-nearest neighbor decision rule, support vector machine, naive bayes etc. using various TREC and Reuter corpora. The empirical results have shown that the proposed method performs significantly better than the other classifiers for text categorization.