Machine Learning
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Exploring Gaps in DeepFool inSearch of More Effective Adversarial Perturbations
(2021)Adversarial examples are inputs subtly perturbed to produce a wrong prediction in machine learning models, while remaining perceptually similar to the original input. To find adversarial examples, some attack strategies ... 
Delineation of site‐specific management zones using estimation of distribution algorithms
(2021)In this paper, we present a novel methodology to solve the problem of delineating homogeneous sitespecific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for ... 
On the fair comparison of optimization algorithms in different machines
(2021)An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to ... 
Efficient metaheuristics for spacecraft trajectory optimization
(202103)Metaheuristics has a long tradition in computer science. During the past few years, different types of metaheuristics, specially evolutionary algorithms got noticeable attention in dealing with realworld optimization ... 
Algorithms for Large Orienteering Problems
(202101)In this thesis, we have developed algorithms to solve largescale Orienteering Problems. The Orienteering Problem is a combinatorial optimization problem were given a weighted complete graph with vertex profits and a maximum ... 
A simulation framework for orbit propagation and space trajectory visualization
(2021)In this paper, an interactive tool for simulation of satellites dynamics and autonomous spacecraft guidance is presented. Different geopotential models for orbit propagation of Earthorbiting satellites are provided, which ... 
Minimax Classification with 01 Loss and Performance Guarantees
(20201201)Supervised classification techniques use training samples to find classification rules with small expected 01 loss. Conventional methods achieve efficient learning and outofsample generalization by minimizing surrogate ... 
Analysis of the sensitivity of the EndOfTurn Detection task to errors generated by the Automatic Speech Recognition process.
(2021)An EndOfTurn Detection Module (EOTDM) is an essential component of au tomatic Spoken Dialogue Systems. The capability of correctly detecting whether a user’s utterance has ended or not improves the accuracy in interpreting ... 
Probabilistic Load Forecasting Based on Adaptive Online Learning
(2020)Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent ... 
Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer.
(20201229)The aim of this paper is to analyze the sequence of actions in the health system associated with a particular disease. In order to do that, using Electronic Health Records, we define a general methodology that allows us ... 
Migration in MultiPopulation Differential Evolution for Many Objective Optimization
(2020)The paper proposes a novel extension of many objective optimization using differential evolution (MaODE). MaODE solves a many objective optimization (MaOO) problem by parallel optimization of individual objectives. MaODE ... 
QLearning Induced Artificial Bee Colony for Noisy Optimization
(2020)The paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution ... 
A Machine Learning Approach to Predict Healthcare Cost of Breast Cancer Patients
(2021)This paper presents a novel machine learning approach to per form an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: i) in ... 
Journey to the center of the linear ordering problem
(202006)A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the ... 
Mutual information based feature subset selection in multivariate time series classification
(2020)This paper deals with supervised classification of multivariate time se ries. In particular, the goal is to propose a filter method to select a subset of time series. Consequently, we adopt the framework proposed by Brown ... 
indepth analysis of SVM kernel learning and its components
(2020)The performance of support vector machines in nonlinearlyseparable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs ... 
General supervision via probabilistic transformations
(20200801)Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ... 
Application of machine learning techniques to weather forecasting
(20181024)Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the ... 
Theoretical and Methodological Advances in Semisupervised Learning and the ClassImbalance Problem
(20181130)his paper focuses on the theoretical and practical generalization of two known and challenging situations from the field of machine learning to classification problems in which the assumption of having a single binary class ... 
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
(202007)The Quadratic Assignment Problem (QAP) is a wellknown permutationbased combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NPhard ...