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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)

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)

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)

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)

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)

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)

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)

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)

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)

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 ...

A Deep Learning Approach for Generating Soft Range Information from RF Data