Learning Performance of Nonlinear Classification Models Based on Markov Sampling

Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems. This paper investigates the learning performance of nonlinear classification models based on Markov sampling, which builds upon the traditional framework using i.i.d. samples. Subsequently, we introduce a ueMC-NL algorithm, tailored specifically for nonlinear classification models, facilitating the production of ueMC samples from a finite dataset. Numerical investigations on the random forest and the MLP model reveal that nonlinear classification models utilizing ueMC samples yield lower misclassification rates compared to i.i.d. samples.