Recent Submissions

  • Hybridizing Cartesian Genetic Programming and Harmony Search for Adaptive Feature Construction in Supervised Learning Problems 

    Elola A.; Del Ser J.; Bilbao M.; Perfecto C.; Alexandre E.; Salcedo-Sanz S. (Applied Soft Computing, 2017-02-28)
    The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very di- verse approaches. In this context this work focuses on the automatic con- ...
  • On the relevance of preprocessing in predictive maintenance for dynamic systems 

    Cernuda C. (Predictive Maintenance in Dynamic Systems, 2018)
    The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, ...
  • 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 ...
  • Spatiotemporal information coupling in network navigation 

    Mazuelas S.; Shen Y.; Win Z. (IEEE Transactions on Information Theory, 2018-12)
    Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...
  • 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 ...
  • A review on distance based time series classification 

    Abanda A.; Mori U.; Lozano JA. (Data Mining and Knowledge Discovery,, 2018-11-01)
    Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and ...
  • 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 ...
  • 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 ...
  • Spacecraft Trajectory Optimization: A review of Models, Objectives, Approaches and Solutions 

    Shirazi A.; Ceberio J.; Lozano J.A. (Progress in Aerospace Sciences, 2018)
    This article is a survey paper on solving spacecraft trajectory optimization problems. The solving process is decomposed into four key steps of mathematical modeling of the problem, defining the objective functions, ...
  • A note on the behavior of majority voting in multi-class domains with biased annotators 

    Hernández-González J.; Inza I.; Lozano J.A. (IEEE Transactions on Knowledge and Data Engineering, 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 ...
  • 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 ...
  • On-Line Dynamic Time Warping for Streaming Time Series 

    Oregi I.; Pérez A.; Del Ser J.; Lozano J.A. (Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, 2017-09)
    Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning ...
  • 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 ...
  • Nature-inspired approaches for distance metric learning in multivariate time series classification 

    Oregui I.; Del Ser J.; Pérez A.; Lozano J.A. (IEEE Congress on Evolutionary Computation (CEC), 2017-07)
    The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular ...
  • An efficient approximation to the K-means clustering for Massive Data 

    Capo M.; Pérez A.; Lozano J.A. (Knowledge-Based Systems, 2017-02-01)
    Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...
  • Efficient approximation of probability distributions with k-order decomposable models 

    Pérez A.; Inza I.; Lozano J.A. (International Journal of Approximate Reasoning, 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 ...
  • Soft range information for network localization 

    Mazuelas S.; Conti A.; Allen J.C.; Win M.Z. (IEEE Transactions on Signal Processing, 2018-06-15)
    The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on ...
  • Multi-objectivising Combinatorial Optimisation Problems by means of Elementary Landscape Decompositions 

    Ceberio J.; Calvo B.; Mendiburu A.; Lozano JA. (Evolutionary Computation, 2017-12)
    In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. ...
  • A system for airport weather forecasting based on circular regression trees 

    Rozas-Larrondo P.; Inza I.; Lozano J.A. (Environmental Modelling & Software, 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 ...

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