Significant attention has been drawn to support vector data description (SVDD) due to its exceptional performance in one-class classification and novelty detection tasks. Nevertheless, all slack variables are assigned the same weight during the modeling process. This can lead to a decline in learning performance if the training data contains erroneous observations or outliers. In this study, an extended SVDD model, Rescale Hinge Loss Support Vector Data Description (RSVDD) is introduced to strengthen the resistance of the SVDD to anomalies. This is achieved by redefining the initial optimization problem of SVDD using a hinge loss function that has been rescaled. As this loss function can increase the significance of samples that are more likely to represent the target class while decreasing the impact of samples that are more likely to represent anomalies, it can be considered one of the variants of weighted SVDD. To efficiently address the optimization challenge associated with the proposed model, the half-quadratic optimization method was utilized to generate a dynamic optimization algorithm. Experimental findings on a synthetic and breast cancer data set are presented to illustrate the new proposed method’s performance superiority over the already existing methods for the settings considered.