The success of machine-learning methods is spreading their use to many different fields. This paper analyses one these methods, the Kernel Logistic Regression (KLR), from point view Random Utility Model (RUM) and proposes KLR specify utilities in RUM, freeing modeler need postulate a functional relation between features. A Monte Carlo simulation study conducted empirically compare with Multinom...

The ensemble machine learning methods incorporating random subspace and random forest employing genetic fuzzy rule-based systems as base learning algorithms were developed in Matlab environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The accuracy of ensembles generate...

BACKGROUND For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropria...