Self-Optimizing Radial Basis Function Support Vector Classifier (SO-RBFSVC)

Support vector classifiers (SVCs) typically use radial basis function (RBF) kernels to map data into higher dimensional spaces that may improve the linear separation of otherwise nonseparable classes. We present a novel self-optimizing radial basis function support vector classifier (SO-RBFSVC) that integrates response surface methodology (RSM), two-dimensional cubic spline interpolation, and bootstrapped Latin partitions (BLPs) for automated hyperparameter tuning. The SO-RBFSVC simultaneously optimizes the RBF kernel width (σ) and cost parameter (C) using an interpolated response surface obtained from generalized prediction accuracies. The SO-RBFSVC was compared to other self-optimizing classifiers (super SVC [sSVC] and super partial least squares discriminant analysis [sPLS-DA]). Four datasets were evaluated: (i) hemp and marijuana discrimination using proton nuclear magnetic resonance spectra, (ii) barley growth location using near-infrared spectra, (iii) glass-type identification based on elemental composition, and (iv) wine cultivar classification from physicochemical properties. External validation results showed that SO-RBFSVC performed comparably to the other models, achieving error rates of 0.4 ± 0.5% for hemp/marijuana, 7 ± 1% for glass, and 6 ± 1% for wine, while outperforming the linear models with 10 ± 1% error for the barley NIR data. For the first time, generalized sensitivity analysis (GSA) was applied to quantify model linearity. GSA revealed high nonlinearity in the barley dataset, justifying a nonlinear model. The SO-RBFSVC provides robust, automated classifier tuning for low- and high-dimensional datasets, offering ease of use.