Several experiments aimed to apply recently proposed statistical procedures which are recommended for analysing multiple 1×n and n×n comparisons of machine learning algorithms were conducted. 11 regression algorithms comprising 5 deterministic and 6 neural network ones implemented in the data mining system KEEL were employed. All experiments were performed using 29 benchmark datasets for regres...

Semi-supervised learning approaches are trained using the full training (labeled) data and available testing (unlabeled) data. Demonstrations of the value of training with unlabeled data typically depend on a smoothness assumption relating the conditional expectation to high density regions of the marginal distribution and an inherent missing completely at random assumption for the labeling. So...

This thesis considers optimization techniques with applications in assignment and generalized linear regression problems. The first part of the thesis investigates the worst-case robust counterparts of combinatorial optimization problems with least squares (LS) cost functions, where the uncertainty lies on the linear transformation of the design variables. We consider the case of ellipsoidal un...

This paper proposes an approach to the identification of evolving fuzzy Takagi–Sugeno systems based on the optimally pruned extreme learning machine (OP-ELM) methodology. First, we describe ELM, a simple yet accurate learning algorithm for training single-hidden layer feed-forward artificial neural networks with random hidden neurons. We then describe the OP-ELM methodology for building ELM mod...

Support Vector Machines (SVMs) for regression problems are trained by solving a quadratic optimization problem which needs on the order of l memory and time resources to solve, where l is the number of training examples. In this paper, we propose a decomposition algorithm, SVMTorch, which is similar to SVM-Light proposed by Joachims (1999) for classi cation problems, but adapted to regression p...

Abstract: Sparse models for high-dimensional linear regression and machine learning have received substantial attention over the past two decades. Model selection, or determining which features or covariates are the best explanatory variables, is critical to the interpretability of a learned model. Much of the current literature assumes that covariates are only mildly correlated. However, in mo...

The problem of selecting an adequate set of variables from a given data set of a sampled function, becomes crucial by the time of designing the model that will approximate it. Several approaches have been presented in the literature although recent studies showed how the Delta Test is a powerful tool to determine if a subset of variables is correct. This paper presents new methodologies based o...