A new Mercer sigmoid kernel for clinical data classification

Abstract

In classification with Support Vector Machines, only Mercer kernels, i.e. valid kernels, such as the Gaussian RBF kernel, are widely accepted and thus suitable for clinical data. Practitioners would also like to use the sigmoid kernel, a non-Mercer kernel, but its range of validity is difficult to determine, and even within range its validity is in dispute. Despite these shortcomings the sigmoid kernel is used by some, and two kernels in the literature attempt to emulate and improve upon it. We propose the first Mercer sigmoid kernel, that is therefore trustworthy for the classification of clinical data. We show the similarity between the Mercer sigmoid kernel and the sigmoid kernel and, in the process, identify a normalization technique that improves the classification accuracy of the latter. The Mercer sigmoid kernel achieves the best mean accuracy on three clinical data sets, detecting melanoma in skin lesions better than the most popular kernels; while with non-clinical data sets it has no significant difference in median accuracy as compared with the Gaussian RBF kernel. It consistently classifies some points correctly that the Gaussian RBF kernel does not and vice versa.

Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation

“…Based on experiments we focused our analysis on three models: (i) the top performing algorithm by AUC, a support vector machine (SVM) with a Mercer sigmoid kernel [76], (ii) a common statistical algorithm, penalized logistic regression with ridge/L1 loss, and (iii) a random forest model with a small batch size.…”