In this paper, we tailor optimal randomized regression trees to handle multivariate functional data. A compromise between ...

This PhD dissertation bridges the disciplines of Operations Research and Machine Learning by developing novel Mathematical ...

Functional data appear more and more in modern societies, in which many processes are monitored in real time. Classification ...

Explainability and causality are becoming increasingly relevant in Machine Learning research. On the one hand, given the ...

Counterfactual Analysis is a recent field of Explainable Machine Learning focused on the development of methods to provide ...

Many real-life applications consider nominal categorical predictor variables that have a hierarchical structure, e.g. ...

In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between ...

The Naïve Bayes is a tractable and efficient approach for statistical classification. In general classification problems, ...

The objective of this PhD dissertation is the development of new models for Supervised Classification and Benchmarking, ...

This PhD dissertation bridges the disciplines of Operations Research and Statistics to develop novel computational methods ...

The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, ...

En el ámbito de las matemáticas y la ingeniería aparecen frecuentemente integrales multidimensionales que son difíciles ...

Over the years, when speaking about prediction models, the focus has been set on improving their accuracy, at the cost of ...

The regression problem with a large number of variables appears in various fields of science, sparse methods make this ...

Survival Analysis is a crucial problem in statistics that tries to explain and model the behaviour of the individuals of ...

Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base ...

Classification and regression trees, as well as their variants, are off-the-shelf methods in Machine Learning. In this ...

The Naïve Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, ...

In this paper, we tackle the problem of enhancing the interpretability of the results of Cluster Analy sis. Our goal is ...

Dynamic optimisation provides complex challenges for optimal solution, but greatly increases applicability when considering ...

Classifcation and regression trees, as well as their variants, are of-the-shelf meth ods in Machine Learning. In this ...

In this article we consider an aggregate loss model with dependent losses. The loss oc- currence process is governed by a ...

Identifying key members in a social network is critical to understand the underlying system behavior. Whereas there are ...

Random forests are considered a fundamental tool in supervised learning. Conse quently, random forests are used in a wide ...

When continuously monitoring processes over time, data is collected along a whole period, from which only certain time ...

This research was financially supported by CRIDA, who also provided us data for this research. This master thesis has been ...

One of the main challenges researchers face is to identify the most relevant features in a prediction model. As a consequence, ...

The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. ...

Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a ...

Renewable energy technology has seen great advances in recent decades, combined with an ever increasing interest in the ...

This paper investigates how the production policy, as well as other factors, affect the facility location-allocation ...

In this paper we propose an optimization model and a solution approach to visualize datasets which are made up of individuals ...

Mathematical Optimization plays a pillar role in Machine Learning (ML) and Neural Networks (NN) are amongst the most popular ...

When classification methods are applied to high-dimensional data, selecting a subset of the predictors may lead to an ...

El objetivo de esta tesis doctoral es desarrollar nuevos métodos para la clasificación y regresión supervisada en el ...

Soiling of heliostat surfaces due to local climate has a direct impact on their optical efficiency and therefore a direct ...

Optimising the aiming strategy is crucial for Solar Power Tower plants, in order to maximise the energy generated, whilst ...

Functional Data Analysis (FDA) is devoted to the study of data which are functions. Support Vector Ma- chine (SVM) is a ...

Inclement weather effects have a direct impact on the efficiency of a Solar Power Tower plant and have the potential to ...

Support vector machine (SVM) is a powerful tool in binary classification, known to attain excellent misclassification ...

In this work, Spin Glasses with Ising Sping model are studied. The objetive is the computation of the magnetic partition ...

In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural ...

Currently we find regression problems in many branches of science, so, as better is the model we use to select variables, ...

The sample average approximation (SAA) method is an approach for solving stochastic optimization problems by using Monte ...

Feature Selection (FS) is a crucial procedure in Data Science tasks such as Classification, since it identifies the relevant ...

In this paper, we address the problem of visualizing a frequency distribution and an adjacency relation attached to a set ...

The subject of this document is Bayesian Inference, an inference system based on Bayes’ Formula. In the first chapter we ...

The distribution of temperature on a Solar Power Tower (SPT) plant receiver directly affects the lifespan of the structure ...

This PhD dissertation focuses on developing new Mathematical Optimization models and solution approaches which help to ...

Muchos problemas de la vida real pueden modelarse como problemas de clasificación, tales como la detección temprana de ...

Vector autoregressive (VAR) models constitute a powerful and well studied tool to analyze multivariate time series. Since ...

Finding robust solutions of an optimization problem is an important issue in practice, and various con- cepts on how to ...

In this paper we address the problem of visualizing a set of individuals, which have attached a statistical value given ...

The design of a Solar Power Tower plant involves the optimization of the heliostat field layout. Fields are usually designed ...

In this paper we address the problem of visualizing in a bounded region a set of individuals, which has attached a ...

The p-facility Huff location problem aims at locating facilities on a competitive environment so as to maximize the market ...

A fundamental problem in the analysis of chemical reactions networks consists of identifying concentration values along ...

Covering problems are well studied in the Operations Research literature under the assumption that both the set of users ...

Esta tesis se centra en derivar métodos robustos o dispersos bajo la perspectiva de la optimización para problemas que ...

En los últimos años, la ciencia de los datos se ha convertido en una herramienta muy importante para tratar datos, así ...

Model inference is a challenging problem in the analysis of chemical reactions networks. In order to empirically test ...

The ability of a firm to produce goods and/or services and market them effectively depends largely on the location of its ...

In this paper we consider the location of stops along the edges of an already existing public transportation network. The ...

In this paper the optimization of a heliostat field with triangular heliostat pods is addressed. The use of structures ...

This paper explores the classic single-item newsvendor problem under a novel setting which combines temporal dependence ...

In this paper we consider multifacility Huff facility location problem on networks. First, we introduce a slight modification ...

In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are ...

The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster ...

El diseño de plantas solares con tecnología de torre (utilizaremos las siglas en inglés SPT) comenzó a ser estudiado en ...

Statistical learning plays a key role in many areas of science, finance and industry. In particular, supervised learning ...

We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the ...

In this article a new procedure to optimize the design of a Multiple Receivers Solar Power Tower system is presented. The ...

A heuristic method for optimizing a solar power tower system is proposed, in which both heliostat field (heliostat locations ...

The two best studied facility location problems are the p-median problem and the uncapacitated facility location problem ...

This PhD dissertation focuses on the study of Mixed Integer Nonlinear Programming (MINLP) problems [34] for two important ...

Principal Components are usually hard to interpret. Sparseness is considered as one way to improve interpretability, and ...

It is well known that some local search heuristics for K-clustering problems, such as k-means heuristic for minimum ...

Recently, Support Vector Machines with the ramp loss (RLM) have attracted attention from the computational point of view. ...

The default approach for tuning the parameters of a Support Vector Machine (SVM) is a grid search in the parameter space. ...

One recently proposed criterion to separate two data sets in Classification is to use a hyperplane that minimizes the sum ...

In this paper we present a collection of combinatorial optimization problems that allows to assess the vulnerability of ...

The Markovian arrival process (MAP) is a stochastic process that allows for modeling dependent and non-exponentially ...

Where to locate one or several facilities on a network so as to minimize the expected users-closest facility transportation ...

A facility is to be located in the Euclidean plane to serve certain sites by covering them closely. Simultaneously, a set ...

The double Pareto Lognormal(dPlN) statistical distribution, defined interms of both an exponentiated skewed Laplace ...

Euclidean Minimum Sum-of-Squares Clustering amounts to finding p prototypes by minimizing the sum of the squared Euclidean ...

Huff location problems have been extensively analyzed within the field of competitive continuous location. In this work, ...

A two-class classification problem is considered where the objects to be classified are bags of instances in d-space. The ...

Principal component analysis is a popular data analysis dimensionality reduction technique, aiming to project with minimum ...

Most time series forecasting methods assume the series has no missing values. When missing values exist, interpolation ...

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, ...

In the (r | p)-centroid problem, two players, called leader and follower, open facilities to service clients. We assume ...

We consider multifacility Huff location problems on networks. The mixed integer nonlinear optimization problem is solved using Variable Neighborhood Search and Multi-Start Local Search metaheuristics. Computational experience is reported.

A discrete location problem with nonlinear objective is addressed. A set of p plants is to be open to serve a given set ...

La comunicación aborda el diseño de un modelo estadístico de predicción dinámica de la sequía en Andalucía y su persistencia ...

Variable Neighborhood Search (VNS) has shown to be a powerful tool for solving both discrete and box-constrained continuous ...

In ordinal regression, a score function and threshold values are sought to classify a set of objects into a set of ranked ...

A new continuous location model is presented and embedded in the literature on robustness in facility location. The ...

Monetary Unit Sampling (MUS), also known as Dollar-Unit Sampling, is a popular sampling strategy in Auditing, in which all ...

The widely used Support Vector Machine (SVM) method has shown to yield good results in Supervised Classification problems. ...

A global optimization procedure is proposed to find a line in the Euclidean three-dimensional space which minimizes the ...

We address the following single-facility location problem: a firm is entering into a market by locating one facility in a ...

Finding p prototypes by minimizing the sum of the squared distances from a set of points to its closest prototype is a ...

A center hyperplane in the d-dimensional space minimizes the maximum of its distances from a finite set of points A with ...

In this note we address the problem of determining selection probabilities for multipurpose surveys, when the aim is the ...

We consider the linear classification method consisting of separating two sets of points in d-space by a hyperplane. We ...

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification ...

It is well known that, if a vector-valued function can be written as difference of componentwise convex functions, the ...

The Big Triangle Small Triangle method has shown to be a powerful global optimization procedure to address continuous ...

We address the problem of locating objects in the plane such as segments, arcs of circumferences, arbitrary convex sets, ...

A computational fluid dynamic model is used to analyze the transport processes of a passive scalar generated in the mixing ...

One recently proposed criterion to separate two datasets in discriminant analysis, is to use a hyperplane which minimises ...

Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we ...

We investigate the effects of dimensionality reduction using different techniques and different dimensions on six two-class ...

En esta tesis, desarrollamos varias herramientas para resolver problemas de clasificación y regresión donde los elementos ...

The nearest-neighbor classifier has been shown to be a powerful tool for multiclass classification. We explore both ...

The two main and contradicting criteria guiding sampling design are accuracy of estimators and sampling costs. In stratified ...

Several multi-criteria-decision-making methodologies assume the existence of weights associated with the different criteria, ...

In this paper we propose a biobjective model for two-group classification via margin maximization, in which the margins ...

Some multiple-criteria decision making methods rank actions by associating weights to the different criteria or actions, ...

The two main and contradicting criteria guiding sampling design are accuracy of estimators and sampling costs. In stratified ...

En esta Tesis Doctoral, presentamos algunas propuestas en las que las herramientas de la Programación Matemática se usan ...

A key problem in Multiple-Criteria Decision Making is how to measure the importance of the different criteria when just a ...

A single facility has to be located in competition with fixed existing facilities of similar type. Demand is supposed to ...

Goal Programming with fractional objectives can be reduced to mathematical programming with a linear objective under linear ...

Let A be a nonempty finite subset of the plane representing the geographical coordinates of a set of demand points (towns, ...

We explore how a simple linear change of variable affects the inclusion functions obtained with Interval Analysis methods. ...

El desarrollo de técncias que permitan clasificar entes (seres vivos, elementos, problemas, ... ) en distintas categorías ...

In this paper we address a general Goal Programming problem with linear objectives, convex constraints, and an arbitrary ...

In this paper the linear regression problem is studied in the context of vector optimization theory. The set of Pareto-optimal ...

In this paper we address the problem of locating a mobile response unit when demand is distributed according to a random ...

In this paper we address the biobjective problem of locating a semiobnoxious facility, that must provide service to a given ...

In this paper, we address the problem of locating a series of facilities on a network maximizing the average distance to ...

A well-known strategy for obtaining a lower bound on the minimum of a d.c. function f−g over a compact convex set S⊂ℝn ...

A median hyperplane in d-dimensional space minimizes the weighted sum of the distances from a finite set of points to it. ...

The p-principal points of a random variable X with finite second moment are those p points in R minimizing the expected ...

In this note we address the problem of finding the GM-estimator for the location parameter of a univariate random variable. ...

Covering methods constitute a broad class of algorithms for solving multivariate Global Optimization problems. In this ...

In this paper, we show that a DC representation can be obtained explicitly for the composition of a gauge with a DC mapping, ...

It is well-known that some of the classical location problems with polyhedral gauges can be solved in polynomial time by ...

In this paper we address the problem of finding a dominator for a multiple-objective maximization problem with quasiconvex ...

An undesirable facility is to be located within some feasible region of any shape in the plane or on a planar network. ...

A number of methods for multiple-objective optimization problems (MOP) give as solution to MOP the set of optimal solutions ...

A location is sought within some convex region of the plane for the central site of some public service to a finite number ...

We address the problem of locating an undesirable facility within a compact set by minimizing a strictly decreasing boundedly ...

In this paper we consider a loss system where the arrivals can be classified into different groups according to their ...

A number of optimization methods require as a rst step the construction of a dominating set (a set containing an optimal ...

In this paper we address the problem of locating p mobile service units in an n-dimensional space minimizing the expected ...

In this note we show by means of a simple example that, if the maximin problem with (nonlinear) concave increasing utility ...

In this paper we address a generalization of the Weber problem, in which we seek for the center and the shape of a rectangle ...

In the last decades there has been an increasing interest in environmental topics. This interest has been reflected in ...

In this paper we address a planar p-facility location problem where, together with a metric induced by a gauge, there ...

This paper is devoted to the study of the Regional Weber Problem, an extension of the Weber problem which allows the demand ...

In this note we present a characterization of halfspace depth which relates it with well-known concepts of Locational ...

In this paper we characterize the set of efficient points in the planar point-objective location problem under a convex ...

In this paper we study a generalized Weber problem where both demand locations and the facility to be located may be regions ...

In this paper we address the problem of ranking a set of alternatives with partial information about the weighting ...

A new and simple methodology is proposed to solve both constrained and unconstrained planar continuous single-facility ...

Least median of squares regression has shown to be an extremely useful tool in robust regression analysis. In this note, ...

In this paper we address the problem of ranking a set of alternatives with partial information about the weighting ...

In this note we present geometrical characterizations for the set of efficient, weakly efficient and properly efficient ...

El objetivo del presente trabajo es el estudio de ciertos aspectos del problema multiobjetivo P (A, S) cuando el conjunto ...

We develop a decision-making methodology for hierarchical structures. It provides different decision makers with ...

En el presente trabajo establecemos una nueva aproximación a la solución del problema de localización con normas mixtas a ...