فرزاد نیکنام - MSc. Student, Department of Computer Engineering, Faculty Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
علی اکبر نیک نفس - Assistant Prof., Department of Computer Engineering, Faculty Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Nowadays, researches are faced with large volumes of data. Since a considerable amount of them are unstructured, they cannot be processed naturally. Hence two main challenges in this field are high dimensional of features space and bulk of available data. In this research, a feature selection method based on target features is propose to handle the curse of dimensionality. Moreover, to address the huge volume of data some of prototype selection approaches are utilized. The proposed method in this paper has three essential steps that each step improves the previous ones. Although, the proposed method reached significant results in each phase separately, its best performance obtained via the last phase in terms of classification accuracy rate. To evaluate the performance of the proposed method, it has been compared with three-layer algorithm. The results revealed that the proposed method had significantly better results than the three-layer algorithm in average.  

Prototype Selection, Market Prediction, Text Classification, Text mining