Using a hybrid meta-evolutionary rule mining approach as a classification response model

Data mining usually means the approaches and appliances for the valid new knowledge discovery from databases. A response model can be built as a decision model for prediction or classification of a domain problem potential like expert systems. In this paper, a hybrid meta-evolutionary rule mining based approach to assess numerical data pattern in the classification problems is proposed for extracting the decision rules including the predictors, the corresponding inequalities and parameters simultaneously so as to building a decision-making model with maximum classification accuracy. In real world, problems are highly nonlinear in nature so that it’s hard to develop a comprehensive model taking into account all the independent variables through the conventional statistical methods. Recently, nonlinear and complex machine learning approaches such as neural networks and support vector machines have been demonstrated to be with more reliable than the conventional statistical approaches. Although the usefulness of using neural networks and support machines has been reported in literatures, the most obstacles are in model building and use of model in which the classification rules are hard to be realized. Through two numerical experiments, we compared our results against the commercial data mining software and other methods in literature, and then we show experimentally that the proposed approach is promising for improving prediction accuracy and enhancing the modeling simplicity.