Summary: In real-world problems, data suffer from measurement errors, data staleness, and repeated measurements, which make data uncertain. To consider the uncertainty of data, the concept of giving each data feature as a multidimensional Gaussian distribution has been utilized. In 2021, support vector machines with an \(\epsilon\) insensitive zone pinball loss (UPinSVMs) for uncertain data classification was proposed, where \(\epsilon\) is a positive number. The UPinSVMs bring noise insensitivity, stability for resampling, and increased model sparsity. However, the value of \(\epsilon\) is known to be specified. In order to improve the performance, we combine the generalized pinball loss (\((\epsilon_1, \epsilon_2)\)-Mod-Pin-SVM) into the uncertain classification, term as UGPinSVMs, where \(\epsilon_1\), \(\epsilon_2\) are two positive numbers. The generalized pinball loss is an optimal insensitive zone pinball loss and is an extension of existing loss functions that also addresses the issues of noise insensitivity and resampling instability. We solve the primal quadratic programming problems by transforming individuals into unconstrained optimization using an efficient stochastic gradient descent algorithm. More specially, we have introduced verified theorems that are related to our approaches and investigated scatter minimization. The results from several benchmark datasets show that our model outperforms the existing classifier in terms of accuracy and statistical analysis. Furthermore, the application of our framework to the crop recommendation dataset is also examined.
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