Many data science issues have to be addressed through unbalanced datasets. Indeed, it may be quite affordable to gather data on the representatives of a given pathology in medicine, or positive operating scenarios of machines in the industry [1]. The related complementary occurrences are, by contrast, scarce and/or expensive to raise. The practice of One-Class Classification (OCC) has been developed within this consideration [1, 2]. One-class classifiers are trained on a single class sample, in the possible presence of a few counterexamples. The related issue consists of understanding and isolating a given class from the rest of the universe. The resulting model allows to predict target (or positive) patterns and to reject outlier (or negative) ones. One-Class Support Vector Machine (OCSVM) is a popular OCC method [3, 4]. Statistics-based techniques such as Gaussian models and Kernel Density Estimation (KDE) [5] are also commonly considered as respectively parametric and nonparametric approaches to estimate a sample distribution.