A Hybrid Approach for Classification of Text Documents Using Naïve Bayes and Instance-Based Learning

In this paper, an enhanced classification model using Naive Bayes with various similarity measures is proposed. In this work, the Bayes formula is used to vectorize a text document such that the vector gives the probability distribution of a document with respect to each predetermined probable category to which the document may belong to. The probability distribution may assign the document to any of the topics, for example, the ones that are found in ‘mini-20-newsgroup’. The proposed approach can be used on any document collections and will show an improved classification accuracy thereby competing with some of the well-known classifiers such as SVM.