Hybrid SVM-GPs learning for modeling of molecular autoregulatory feedback loop systems with outliers

In this paper, the hybrid support vector machines (SVM) and Gaussian process (GPs) are proposed to deal with the molecular autoregulatory feedback loop systems with outliers. In the proposed approach, there are two-stage strategies. In the stage 1, the support vector machine regression (SVMR) approach is used to filter out the outliers in the training data set. Because of the large outliers in the training data set are almost removed, the large outlier's effects are reduce, so the concepts of robust statistic theory are not used to reduce the outlier's effects. The rest of the training data set after the stage 1 is directly used to training the Gaussian process for regression (GPR) in the stage 2. According to the simulation results, the performance of the proposed approach is superior to the least squares support vector machines for regression, and GPR when the outliers are existed in the molecular autoregulatory feedback loop systems.