Effects of Shannon entropy and J-Measurement for making rules by using decision list method

Expert system makes lot of changes in our daily life. For making an expert system we need perfect, efficient and concise knowledge base system (KBS). The backbone of any KBS is the finest, optimum and exact ‘rules’ for any particular application that makes the success of the expert system. In this paper, we generate a few rules that come from a Soybean data set with different effect of Shannon entropy and J measurement by using as the rule evaluation parameter. The dissimilar out comes makes differentiate of the effect by using rule evaluation parameter. The experimental results are also focused the different error rate from Shannon entropy and J measurement and other effects that make the changes in the output. After that, we compare and evaluate the results and outputs from two cases, that represent in graph based layout and tabular representation. More over, a short review of KBS, entropy, decision list are also paying attention in this paper.