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New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment Problem
Benavides, X.
; Ceberio, J.; Hernando, L.; Lozano, J.A. (2023-07-15)
; Ceberio, J.; Hernando, L.; Lozano, J.A. (2023-07-15)
Previous works have shown that studying the characteristics of the Quadratic Assignment Problem (QAP) is a crucial step in gaining knowledge that can be used to design tailored meta-heuristic algorithms. One way to analyze ...
Learning a logistic regression with the help of unknown features at prediction stage
Martinez, M.; Inza, I.; Lozano, J.A. (2023)
; Inza, I.; Lozano, J.A. (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 ...
Learning the progression patterns of treatments using a probabilistic generative model
Zaballa, O.; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)
; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; Lozano, J.A. (2022-12-15)
Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model ...
Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques
Shirazi, A.; Ceberio, J.; Lozano, J.A. (2022-10-09)
; Ceberio, J.; Lozano, J.A. (2022-10-09)
In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs ...
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)
; García-Gutierrez, S.; Armañanzas, R.; 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 ...
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Alvarez, V.; Mazuelas, S.; Lozano, J.A. (2022-07)
; 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 ...
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 ...
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 ...
EDA++: Estimation of Distribution Algorithms with Feasibility Conserving Mechanisms for Constrained Continuous Optimization
Shirazi, A.; Ceberio, J.; Lozano, J.A. (2022-02-25)
; Ceberio, J.; Lozano, J.A. (2022-02-25)
Handling non-linear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear ...
Ad-Hoc Explanation for Time Series Classification
Abanda, A.; Mori, U.; Lozano, J.A. (2022)
; Mori, U.; Lozano, J.A. (2022)
In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and ...