Data Science (DS)
Browse by
Recent Submissions
-
Characterization of rankings generated by pseudo-Boolean functions
(2024)In this paper we pursue the study of pseudo-Boolean functions as ranking generators. The objective of the work is to find new insights between the relation of the degree of a pseudo-Boolean function and the rankings ... -
Fast Computation of Cluster Validity Measures for Bregman Divergences and Benefits
(2023)Partitional clustering is one of the most relevant unsupervised learning and pattern recognition techniques. Unfortunately, one of the main drawbacks of these methodologies refer to the fact that the number of clusters is ... -
Fast K-Medoids With the l_1-Norm
(2023-07-26)K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of ... -
Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees
(2023-12)For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity. The incremental learning of a growing sequence of ... -
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences
(2024)Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to ... -
Speeding-Up Evolutionary Algorithms to Solve Black-Box Optimization Problems
(2024-01-10)Population-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choose the best solutions from a given population ... -
Revisiting Implicit and Explicit Averaging for Noisy Optimization
(2023-10-01)Explicit and implicit averaging are two well-known strategies for noisy optimization. Both strategies can counteract the disruptive effect of noise; however, a critical question remains: which one is more efficient? This ... -
Discretization-Based Feature Selection as a Bilevel Optimization Problem
(2023-08-01)Discretization-based feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization ... -
Double-Weighting for Covariate Shift Adaptation
(2023-07)Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates $x$) of training and testing samples $p_\text{tr}(x)$ and $p_\text{te}(x)$ are different but the label ... -
Challenging test problems for multi- and many-objective optimization
(2023-08-01)In spite of the extensive studies that have been conducted regarding the construction of multi-objective test problems, researchers have mainly focused their interests on designing complicated search spaces, disregarding, ... -
Time consistent expected mean-variance in multistage stochastic quadratic optimization: a model and a matheuristic
(2019)In this paper, we present a multistage time consistent Expected Conditional Risk Measure for minimizing a linear combination of the expected mean and the expected variance, so-called Expected Mean-Variance. The model is ... -
The Natural Bias of Artificial Instances
(2023)Many exact and metaheuristic algorithms presented in the literature are tested by comparing their performance in different sets of instances. However, it is known that when these sets of instances are generated randomly, ... -
Statistical Modelling for Recurrent Events in Sports Injury Research with Applications to Football Injury Data
(2023)Sports injuries stand as undesirable side effects of athletic participation, carrying serious consequences for athletes' health, their professional careers, and overall team performance. With the growing availability of ... -
On the Use of Second Order Neighbors to Escape from Local Optima
(2023-07-12)Designing efficient local search based algorithms requires to consider the specific properties of the problems. We introduce a simple and effi- cient strategy, the Extended Reach, that escapes from local optima ob- tained ... -
Minimum-Fuel Low-Thrust Trajectory Optimization Via a Direct Adaptive Evolutionary Approach
(2023-11-28)Space missions with low-thrust propulsion systems are of appreciable interest to space agencies because of their practicality due to higher specific impulses. This research proposes a technique to the solution of minimum-fuel ... -
Adaptive Estimation of Distribution Algorithms for Low-Thrust Trajectory Optimization
(2023-08-02)A direct adaptive scheme is presented as an alternative approach for minimum-fuel low-thrust trajectory design in non-coplanar orbit transfers, utilizing fitness landscape analysis (FLA). Spacecraft dynamics is modeled ... -
Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers
(2023-09)One particular kind of evolutionary algorithms known as Estimation of Distribution Algorithms (EDAs) has gained the attention of the aerospace industry for its ability to solve nonlinear and complicated problems, particularly ... -
Learning a logistic regression with the help of unknown features at prediction stage
(2023)The use of features available at training time, but not at prediction time, as additional information for training models is known as learning using privileged information paradigm. In this paper, the handling of ...