In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression. We propose a simple and computationally efficient method, called path thresholding (PaTh), that transforms any tuning parameter-dependent sparse regression algorithm into an asymptotically tuning-free sparse regression algorithm. More specifically, we prove that, as the probl...

The aim of this paper is to investigate and optimize surface quality and geometrical characteristics in drilling process of AISI H13 steel, because they are critical items for precision manufacturing. After conducting the experiments, two regression models are developed to extensively evaluate the effect of drilling parameters on process outputs. After that, evolutionary multi-objective optimiz...

OBJECTIVES The aim of the present study was to develop an optimization method of multiple linear regression equation (MLRE), using a genetic algorithm to determine a set of coefficients that minimize the prediction error for the sum of permanent premolars and canine dimensions in a group of young people from a central area of Romania represented by a city called Sibiu. MATERIAL AND METHODS To...

We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the minimum-cost prediction with the onesided loss. The simple approach is accompanied by a solid theoretical guarantee of error transformation, and can be used to cast any one-sided regression method as a costsensitive cla...