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| Feature selection is critical in deep learning aiming to identify the most informative features in dimensionality reduction. In this paper, we propose a novel multi-phase feature selection method, namely Discrimination Improved Correlation-based Feature Selection (DI-CFS), which consists of three modules: the Discrimination Filtering Formula, the Isolation Forest (IF) algorithm, and the Correlation-based Feature Selection (CFS) method. In our method, the Discrimination Filtering Formula is employed to filter out invalid and insignificant features by calculating the discrimination value of them. The IF algorithm is utilized to remove redundant features which are more easily to be partitioned. The point-biserial correlation coefficient is utilized to calculate the weights of different features instead of the Pearson correlation coefficient, and the weights are evaluated by the Correlation-based Feature Selection (CFS) method. The experimental results show that the DI-CFS method is effective. |
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| Keywords:Feature selection; Classification; DI-CFS; Data discrimination; SCADA |
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