Attributes Reduction Based on GA-CFS Method

The selection and evaluation task of attributes is of great importance for knowledge-based systems. It is also a critical factor affecting systems' performance. By using the genetic operator as the searching approach and correlation-based heuristic strategy as the evaluating mechanism, this paper presents a GA-CFS method to select the optimal subset of attributes from a given case library. Based on the above, the classification performance is evaluated by employing the combination method of C4.5 algorithm with k-fold cross validation. The comparative experimental results indicate that the proposed method is capable of identifying the most related subset for classification and prediction with reducing the representation space of the attributes dramatically whilst hardly decreasing the classification precision.