Applying electromagnetism-like mechanism for feature selection

In recent years, methods of feature selection have been increasingly emphasized as venues for reducing cost and shortening the length of time required for computation in data mining. This study utilizes electromagnetism-like mechanism as a wrapper approach to feature selection. Birbil and Fang proposed EM in 2003. EM uses the attraction–repulsion mechanism of the electromagnetism theory to ascertain the optimal solution. Although EM has been applied to the topic of optimization in continuous space and a small number of studies on discrete problems, it has not been applied to the subject of feature selection. In this study, EM combined with 1-nearest-neighbor (1NN) was applied for feature selection and classification. This study utilized the total force exerted on a particle and evaluated this force to determine which features are to be selected. The most crucial features were selected according to the proposed method based on the minimum miss-classification rate, which was attained through 1NN. An unknown datum was classified by 1NN based on the chosen reduced model. To estimate the effectiveness of the proposed method, a numerical experiment was conducted using several data sets with diverse sizes, features, separability, and classes. Experimental results indicated that the proposed method outperformed other well-known algorithms in not only balanced classification accuracy but also efficiency of feature selection. Lastly, this study used an actual case concerning gestational diabetes mellitus to demonstrate the workability of the proposed method.