Manipulating Large Corpora for Text Classification

In this paper, we address the problem of dealing with a large collection of data and propose a method for text classification which manipulates data using two well-known machine learning techniques, Naive Bayes(NB) and Support Vector Machines(SVMs). NB is based on the assumption of word independence in a text, which makes the computation of it far more efficient. SVMs, on the other hand, have the potential to handle large feature spaces, which makes it possible to produce better performance. The training data for SVMs are extracted using NB classifiers according to the category hierarchies, which makes it possible to reduce the amount of computation necessary for classification without sacrificing accuracy.