More accurate diagnosis in electric power apparatus conditions using ensemble classification methods

Recently, the classification study is accelerated, especially in machine learning expertise. Although the decision tree was still recommended as a classification tool in diagnosing electric power apparatus because of the property having the visible if-then rule, the recent development in classification methods, especially those using the ensemble methods, suggests us to apply these methods to condition diagnosis area. In this paper, we report that the new ensemble methods show extremely high accuracy in classification of the electric power apparatus diagnosis, although rule visibility is sacrificed.