• 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 ...

• A Resource Sharing Game for the Freshness of Status Updates 

  Miguelez, F.; Doncel, J.; Ayesta, U. (2021-09-01)

• Rank aggregation for non-stationary data streams 

  Irurozki, E.; Pérez, A.; 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 Deep Learning Approach for Generating Soft Range Information from RF Data 

  li, Y.; Mazuelas, S.; 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 ...

• 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 ...

• Analysis of Dominant Classes in Universal Adversarial Perturbations 

  Vadillo, J.; Santana, R.; Lozano, J.A. (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 ...

• 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 ...

• K-means for Evolving Data Streams 

  Bidaurrazaga, A.; Pérez, A.; Capó, M. (2021-01-01)

  Nowadays, streaming data analysis has become a relevant area of research in machine learning. Most of the data streams available are unlabeled, and thus it is necessary to develop specific clustering techniques that take ...

• 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)

  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 Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform 

  Li, Y.; Mazuelas, S.; 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 ...

• Deep GEM-based network for weakly supervised UWB ranging error mitigation 

  Li, Y; Mazuelas, S.; Shen, Y. (2021-12-30)

  Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learning-based methods for error ...

• 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 Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes] 

  Ircio, J.; Lojo, A.; Mori, U.; Lozano, J.A. (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 ...

• Contributions to Time Series Classification: Meta-Learning and Explainability 

  Abanda, A. (2021-11-16)

  This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In ...

• Location Awareness in Beyond 5G Networks 

  Conti, A.; Morselli, F.; Liu, Z.; Bartoletti, S.; Mazuelas, S.; Lindsey, W.C.; Win, M.Z. (2021-11-01)

  Location awareness is essential for enabling contextual services and for improving network management in 5th generation (5G) and beyond 5G (B5G) networks. This paper provides an overview of the expanding opportunities ...

• Alternative Representations for Codifying Solutions in Permutation-Based Problems 

  Malagon, M.; Irurozki, E.; Ceberio, J. (2020-07-01)

  Since their introduction, Estimation of Distribution Algorithms (EDAs) have proved to be very competitive algorithms to solve many optimization problems. However, despite recent developments, in the case of permutation-based ...

• On solving cycle problems with Branch-and-Cut: extending shrinking and exact subcycle elimination separation algorithms 

  Kobeaga, G.; Merino, M.; Lozano, J.A. (2021-01-01)

  In this paper, we extend techniques developed in the context of the Travelling Salesperson Problem for cycle problems. Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination ...

• Statistical assessment of experimental results: a graphical approach for comparing algorithms 

  Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2021-08-25)

  Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples ...

• A Review on Outlier/Anomaly Detection in Time Series Data 

  Blázquez-García, A.; Conde, A.; Mori, U.; Lozano, J.A. (2021)

  Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for ...

• Water leak detection using self-supervised time series classification 

  Blázquez-García, A.; Conde, A.; Mori, U.; Lozano, J.A. (2021)

  Leaks in water distribution networks cause a loss of water that needs to be com- pensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails ...

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