Correlation-based feature selection methods are popular tools used to select the most important variables to include the true model in the analysis of sparse and high-dimensional models. In application, the presence of anomalous observations in both predictors and responses can seriously jeopardize the prediction accuracy of the model, which in turn leads to misleading interpretations and conclusions if not correctly addressed. Furthermore, the cause of dimensionality is another serious difficulty facing many existing feature selection algorithms. To achieve more reliable feature selection and prediction accuracy, a weighted sure independence screening-based support vector machine for high-dimensional datasets is proposed. The key contribution of our proposed method is that it minimizes the influence of outliers in differentiating between significant and insignificant features and improves predictability and interpretability. Our method consists of three basic steps. In the first step, a weights-based modified reweighted fast, consistent, and high break-down point is computed. The second step utilizes the estimates of weights from the first step to select the most important variables for the model. The third step employs the support vector machine algorithm to calculate prediction values. To demonstrate the effectiveness of the developed procedure, we used both simulation and real-life data examples. Our results show that the proposed methods performs better with a clear margin compared to other procedures.
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