Now showing items 1-20 of 61

    • 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. (Information Sciences, 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 ...
    • An empirical study on collective intelligence algorithms for video games problem-solving 

      Gonzalez-Pardo A.; Palero F.; Camacho D. (Computing and Informatics, 2015-12-31)
      Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bio-inspired algorithms that have been widely used to solve problems in areas like planning, scheduling or ...
    • Analysis of a hybrid Genetic Simulated Annealing strategy applied in multi-objective optimization of orbital maneuvers 

      Shirazi A. (IEEE Aerospace and Electronic Systems Magazine, 2017-03-27)
      Optimization of orbital maneuvers is one of the main issues in conceptual and preliminary design of spacecraft in different space missions. The main issue in optimization of high-thrust orbit transfers is that the common ...
    • Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework 

      Carreño A.; Inza I.; Lozano J.A. (Artificial Intelligence Review, 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 ...
    • Anatomy of the attraction basins: Breaking with the intuition 

      Hernando L.; Mendiburu A.; Lozano J.A. (Evolutionary Computation, 2019)
      olving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood ...
    • Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance 

      Arza E.; Ceberio J.; Pérez A.; Irurozki E. (Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019-07)
      The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ...
    • Are the artificially generated instances uniform in terms of difficulty? 

      Pérez A.; Ceberio J.; Lozano J.A. (IEEE Congress on Evolutionary Computation, 2018-06)
      In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a test-bed to determine the performance of the algorithms at hand. In this context, a recent work on ...
    • Bayesian inference for algorithm ranking analysis 

      Calvo B.; Ceberio J.; Lozano J.A. (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion 6 July 2018, Pages 324-325, 2018-08-30)
      The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about ...
    • Belief Condensation Filtering For Rssi-Based State Estimation In Indoor Localization 

      Mehryary S.; Mazuelas S.; Malekzadehz P.; Spachos P.; Plataniotisy K.N.; Mohammadi A. (2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019)
      Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of ...
    • Calibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptation 

      Nikzad-Langerodi R.; Lughofer E.; Cernuda C.; Reischer T.; Kantner W.; Pawliczek M.; Brandstetter M. (Analytica Chimica Acta, 2018)
      The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of ...
    • Characterising the rankings produced by combinatorial optimisation problems and finding their intersections 

      Hernando L.; Mendiburu A.; Lozano J.A. (GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, 2019)
      The aim of this paper is to introduce the concept of intersection between combinatorial optimisation problems. We take into account that most algorithms, in their machinery, do not consider the exact objective function ...
    • Crowd Learning with Candidate Labeling: an EM-based Solution 

      Beñaran-Muñoz I.; Hernández-González J.; Pérez A. (Conference of the Spanish Association for Artificial Intelligence, 2018-09-27)
      Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case ...
    • Crowd-Centric Counting via Unsupervised Learning 

      Morselli F.; Bartoletti S.; Mazuelas S.; Win M.; Conti A. (2019 IEEE International Conference on Communications Workshops (ICC Workshops), 2019-07-11)
      Counting targets (people or things) within a moni-tored area is an important task in emerging wireless applications,including those for smart environments, safety, and security.Conventional device-free radio-based ...
    • Data generation approaches for topic classification in multilingual spoken dialog systems 

      Montenegro C.; Santana R.; Lozano J.A. (ACM International Conference Proceeding Series, 2019)
      The conception of spoken-dialog systems (SDS) usually faces the problem of extending or adapting the system to multiple languages. This implies the creation of modules specically for the new languages, which is a time ...
    • Detection of Sand Dunes on Mars Using a Regular Vine-based Classification Approach 

      Carrera D.; Bandeira L.; Santana R.; Lozano J.A. (Knowledge- Based Systems, 2018-08)
      This paper deals with the problem of detecting sand dunes from remotely sensed images of the surface of Mars. We build on previous approaches that propose methods to extract informative features for the classification of ...
    • Distance-based exponential probability models on constrained combinatorial optimization problems 

      Ceberio J.; Mendiburu A.; Lozano, J.A. (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, 2018-08-30)
      Estimation of distribution algorithms have already demonstrated their utility when solving a broad range of combinatorial problems. However, there is still room for methodological improvements when approaching constrained ...
    • Early classification of time series by simultaneously optimizing the accuracy and earliness 

      Mori U.; Mendiburu A.; Dasgupta S.; Lozano J.A. (IEEE Transactions on Neural Networks and Learning Systems, 2017-10)
      The problem of early classi cation of time series appears naturally in contexts where the data, of temporal nature, is collected over time, and early class predictions are interesting or even required. The objective is to ...
    • Early classification of time series using multi-objective optimization techniques 

      Mori U.; Mendiburu A.; Miranda I.M.; Lozano J.A. (Information Sciences, 2019-04-23)
      In early classification of time series the objective is to build models which are able to make class-predictions for time series as accurately and as early as possible, when only a part of the series is available. It is ...
    • Effects of reducing VMs management times on elastic applications 

      Pascual J.A.; Lozano J.A.; Miguel-Alonso J. (Journal of Grid Computing, 2018-05)
      Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers ...
    • Efficient approximation of probability distributions with k-order decomposable models 

      Pérez A.; Inza I.; Lozano J.A. (International Journal of Approximate Reasoning, 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 ...