A machine learning based method for optimal journal classification

We present a hypothetical and realistic examination and exploration of a number of bibliometric indicators of journal performance. In this paper, the indicators we have focused upon are Eigenfactor indicator, Impact factor, audience factor and Article influence weight indicator. Our focus is to find the missing parameters and some limitations that have not been conducted in previous algorithms. To find the influential parameters and to propose a new journal performance factor, that ranked a journal in best accepted manner. For classification and verification purpose we use a machine learning classification technique (Bayesian classification). It is one of the most common learning algorithms in machine learning classification. Using bayesain classification, we classify several journals according to our proposed methods and compare results with the previous methods.