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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)
; 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 ...
Optimization of deep learning precipitation models using categorical binary metrics
Larraondo, P.R.; Renzullo, L.J.; Van Dijk, A.I.J.M.; Inza, I.; Lozano, J.A. (2020)
(2020)
This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm ...
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework
Carreño, A.; Inza, I.; Lozano, J.A. (2019-09-01)
; Inza, I.; Lozano, J.A. (2019-09-01)
In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a ...
Aggregated outputs by linear models: An application on marine litter beaching prediction
Hernández-González, J.; Inza, I.; Granado, I.; Basurko, O.C.; Fernández, J.A.; Lozano, J.A. (2019-01-01)
(2019-01-01)
In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning ...
A note on the behavior of majority voting in multi-class domains with biased annotators
Hernández-González, J.; Inza, I.; Lozano, J.A. (2018-05)
(2018-05)
Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples ac- cording to their own criteria. Although it has been extensively studied in the ...
Learning to classify software defects from crowds: a novel approach
Hernández-González, J.; Rodríguez, D.; Inza, I.; Rachel, H.; Lozano, J.A. (2017-11-01)
(2017-11-01)
In software engineering, associating each reported defect with a cate- gory allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using stan- dard ...
A system for airport weather forecasting based on circular regression trees
Rozas-Larrondo, P.; Inza, I.; Lozano, J.A. (2017-11-01)
(2017-11-01)
This paper describes a suite of tools and a model for improving the accuracy of airport weather forecasts produced by numerical weather prediction (NWP) products, by learning from the relationships between previously ...
Measuring the Class-imbalance Extent of Multi-class Problems
Ortigosa-Hernández, J.; Inza, I.; Lozano, J.A. (2017-07-30)
(2017-07-30)
Since many important real-world classification problems involve learning from unbalanced data, the challenging class-imbalance problem has lately received con- siderable attention in the community. Most of the methodological ...
Efficient approximation of probability distributions with k-order decomposable models
Pérez, A.; Inza, I.; Lozano, J.A. (2016-07)
; Inza, I.; Lozano, J.A. (2016-07)
During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...
Efficient approximation of probability distributions with k-order decomposable models
Pérez, A.; Inza, I.; Lozano, J.A. (2016-01-01)
; Inza, I.; Lozano, J.A. (2016-01-01)
During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...