Feature selection for steel defects classification

In this paper, features of steel defects data are selected using a wrapper algorithm to increase classification performance. The data are constructed using images of steel defects which are classified two classes as defects and pseudo defects. The suggested algorithm selects features which are relevant to class using the kappa statistic. This measure is suggested to improve accuracy of minor class because steel defects data are highly imbalanced. The several algorithms were compared with the algorithm to show performances.