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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 ...
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 ...
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 ...
A Deep Learning Approach for Generating Soft Range Information from RF Data
li, Y.; Mazuelas, S.; Shen, Y. (2022-01-24)
; Shen, Y. (2022-01-24)
Radio frequency (RF)-based techniques are widely
adopted for indoor localization despite the challenges in extracting
sufficient information from measurements. Soft range
information (SRI) offers a promising alternative ...
Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework
Armañanzas, R.; Diaz, A.; Martinez, M.; Mazuelas, S. (2022-01-14)
; Diaz, A.; Martinez, M.; Mazuelas, S. (2022-01-14)
The overall global death rate for COVID-19 patients
has escalated to 2.13% after more than a year of worldwide
spread. Despite strong research on the infection pathogenesis, the
molecular mechanisms involved in a fatal ...
A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]
Ircio, J.; Lojo, A.; Mori, U.; Lozano, J.A. (2022)
(2022)
Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this ...
Analysis of Dominant Classes in Universal Adversarial Perturbations
Vadillo, J.; Santana, R.; Lozano, J.A. (2022)
(2022)
The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many differ- ent strategies can be employed to efficiently generate adversarial attacks, some ...
Rank aggregation for non-stationary data streams
Irurozki, E.; Pérez, A.; Lobo, J.L.; Del Ser, J. (2022)
; Lobo, J.L.; Del Ser, J. (2022)
The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution ...
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Li, Y.; Mazuelas, S.; Shen, Y. (2021-12-30)
; Shen, Y. (2021-12-30)
Localization systems based on ultra-wide band
(UWB) measurements can have unsatisfactory performance in
harsh environments due to the presence of non-line-of-sight
(NLOS) errors. Learning-based methods for error ...