(A) study on the Improvement of the $Na\"{i}ve$ Bayes method

Abstract

In this paper, we introduce a new matrix weighting scheme that is applied to a term-document matrix, which is an input matrix of documents required for running the $Na\"{i}ve$ Bayes method, as an effort to improve the accuracy of the $Na\"{i}ve$ Bayes method. We first examine two existing weighting strategies: Term Frequency - Inverse Document Frequency weighting and Golden Words weighting. Next, we present the new weighting method that incorporates the two existing methods with a slight modification in the algorithm. Then, we compare the accuracy of the $Na\"{i}ve$ Bayes method when the three different weighting schemes are applied to the term-document matrix. It is shown through simulation that the new method yields a greater degree of accuracy than the other two weighting methods. In addition, we set different values to the parameter in the new method and examine the change in accuracy. Finally, we find the optimal value of the parameter that maximizes the accuracy of the Na\"ive Bayes method.