Using fuzzy-rough set feature selection for feature construction based on genetic programming

Feature construction can improve the classifier's performance by constructing powerful and distinctive features. Genetic programming algorithm is one the automatic programming methods which provides the possibility of constructing mathematical expressions without any predefined format. As we know, all features of a data set are not suitable; therefore, we believe that if all features are used for feature construction, inappropriate and ineffective features may be constructed. Hence, the main purpose of this paper is firstly, selecting the suitable features, before the construction process, and then constructing a new feature using these selected features. To do so, a fuzzy rough quick feature selection technique is employed. For assessment, the proposed method along with 5 other feature construction methods are applied on 6 standard data sets. The obtained results indicate that the proposed method has more ability in constructing more distinctive features compared to competing approaches.