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A mathematical analysis of EDAs with distance-based exponential models
Unanue, I.; Merino, M.
; Lozano, J.A. (2022-09-01)
; Lozano, J.A. (2022-09-01)
Estimation of Distribution Algorithms have been successfully used to solve permutation-based Combinatorial Optimization Problems. In this case, the algorithms use probabilistic models specifically designed for codifying ...
Learning a Battery of COVID-19 Mortality Prediction Models by Multi-objective Optimization
Martinez, M.; García-Gutierrez, S.; Armañanzas, R.; Díaz, A.; Inza, I.; Lozano, J.A. (2022-07-09)
; Díaz, A.; Inza, I.; Lozano, J.A. (2022-07-09)
The COVID-19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the ...
Implementing the Cumulative Difference Plot in the IOHanalyzer
Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)
; Ceberio, J.; Irurozki, E.; Pérez, A. (2022-07)
The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results ...
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Alvarez, V.; Mazuelas, S.; Lozano, J.A. (2022-07)
; Mazuelas, S.; Lozano, J.A. (2022-07)
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate ...
The role of asymmetric prediction losses in smart charging of electric vehicles
Straka, M.; Buzna, L.; Refa, N.; Mazuelas, S. (2022-07-01)
(2022-07-01)
Climate change prompts humanity to look for decarbonisation opportunities, and a viable option is to supply electric vehicles with renewable energy. The stochastic nature of charging demand and renewable generation requires ...
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
li, Y.; Mazuelas, S.; Shen, Y. (2022-05-23)
; Shen, Y. (2022-05-23)
Deep generative models (DGMs) and their conditional
counterparts provide a powerful ability for general-purpose
generative modeling of data distributions. However, it remains
challenging for existing methods to address ...
An active adaptation strategy for streaming time series classification based on elastic similarity measures
Oregi, I.; Pérez, A.; Del Ser, J.; Lozano, J.A. (2022-05-21)
; Del Ser, J.; Lozano, J.A. (2022-05-21)
In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line ...
Generalized Maximum Entropy for Supervised Classification
Mazuelas, S.; Shen, Y.; Pérez, A. (2022-04)
; Shen, Y.; Pérez, A. (2022-04)
The maximum entropy principle advocates to
evaluate events’ probabilities using a distribution that maximizes
entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ...
Time Series Classifier Recommendation by a Meta-Learning Approach
Abanda, A.; Mori, U.; Lozano, J.A. (2022-03-26)
; Mori, U.; Lozano, J.A. (2022-03-26)
This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set ...
Modeling latent spatio-temporal disease incidence using penalized composite link models
Lee, D.-J.; Durban, M.; Ayma, D.; Van de Kassteele, J. (2022-03-10)
; Durban, M.; Ayma, D.; Van de Kassteele, J. (2022-03-10)
Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, ...