Data Science (DS)
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Contributions to Time Series Classification: MetaLearning and Explainability
(20211116)This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In ... 
Prediction of sports injuries in football: a recurrent timetoevent approach using regularized Cox models
(20211120)Databased methods and statistical models are given special attention to the study of sports injuries to gain indepth understanding of its risk factors and mechanisms. The objective of this work is to evaluate the use of ... 
On the Effect of the Cooperation of IndicatorBased Multiobjective Evolutionary Algorithms
(20210801)For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicatorbased MOEA (IBMOEA) has specific search preferences ... 
Location Awareness in Beyond 5G Networks
(20211101)Location awareness is essential for enabling contextual services and for improving network management in 5th generation (5G) and beyond 5G (B5G) networks. This paper provides an overview of the expanding opportunities ... 
COARSEEMOA: An indicatorbased evolutionary algorithm for solving equality constrained multiobjective optimization problems
(20211201)Many realworld applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision ... 
Parallel MultiObjective Evolutionary Algorithms: A Comprehensive Survey
(20211201)MultiObjective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of ... 
A Novel Parametric benchmark generator for dynamic multimodal optimization
(20210801)In most existing studies on dynamic multimodal optimization (DMMO), numerical simulations have been performed using the Moving Peaks Benchmark (MPB), which is a twodecadeold test suite that cannot simulate some critical ... 
A survey of bias in machine learning through the prism of statistical parity
(2020)Applications based on machine learning models have now become an indispensable part of the everyday life and the professional world. As a consequence, a critical question has recently arose among the population: Do algorithmic ... 
CURIE: a cellular automaton for concept drift detection
(20211101)Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as ... 
Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models
(20210101)A package is introduced that provides the weighted smooth backfitting estimator for a large family of popular semiparametric regression models. This family is known as generalized structured models, comprising, for example, ... 
ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates
(20210101)This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariatespecific ROC curve, and the covariateadjusted ROC curve by different methods, both from (semi) parametric and nonparametric ... 
Phenomics data processing: A plotlevel model for repeated measurements to extract the timing of key stages and quantities at defined time points
(2021)Decisionmaking in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as highthroughput eld phenotyping (HTFP) hold ... 
Alternative Representations for Codifying Solutions in PermutationBased Problems
(20200701)Since their introduction, Estimation of Distribution Algorithms (EDAs) have proved to be very competitive algorithms to solve many optimization problems. However, despite recent developments, in the case of permutationbased ... 
LUNAR: Cellular automata for drifting data streams
(20210108)With the advent of fast data streams, realtime machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods ... 
ATMFCGA: An Adaptive Transferguided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
(20210901)Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts ... 
On solving cycle problems with BranchandCut: extending shrinking and exact subcycle elimination separation algorithms
(20210101)In this paper, we extend techniques developed in the context of the Travelling Salesperson Problem for cycle problems. Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination ... 
Statistical assessment of experimental results: a graphical approach for comparing algorithms
(20210825)Nondeterministic measurements are common in realworld scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples ... 
Simulation approach for assessing the performance of the γEWMA control chart
(20210222)i) Purpose: The purpose of this paper is to evaluate the performance of a modified EWMA control chart ($\gamma$EWMA control chart), which considers data distribution and incorporate its correlation structure, simulating ... 
Altered effective connectivity in sensorimotor cortices: a novel signature of severity and clinical course in depression
(2021)Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function ... 
From habitat to management: a simulation framework for improving statistical methods in fisheries science
(20210707)Monte Carlo simulation consists of computer experiments that involve creating data by pseudorandom sampling and has shown to be a powerful tool for studying the performance of statistical methods. In this thesis Monte ...