Forecasting Feature Selection based on Single Exponential Smoothing using Wrapper Method

Feature selection is one way to simplify classification process. The purpose is only the selected features are used for classification process and without decreasing its performance when compared without feature selection. This research uses new feature matrix as the base for selection. This feature matrix contains forecasting result using Single Exponential Smoothing (FMF(SES)). The method uses wrapper method of GASVM and it is named FMF(SES)-GASVM. The result of this research is compared with other methods such as GA Bayes, Forward Bayes and Backward Bayes. The result shows that FMF(SES)-GASVM has maximum accuracy when compared of FMF(SES)-GA Bayes, FMF(SES)-Forward Bayes, FMF(SES)-Backward Bayes, however the number of selected features are more than if compared with FMF(SES)-GA Bayes and FMF(SES)-Forward Bayes.