Javier Del Ser Lorente

External Scientific Member

Photo not available

T +34 946 567 842
F +34 946 567 842

Information of interest

  • Rank aggregation for non-stationary data streams 

    Irurozki, E.; Pérez, A.Autoridad BCAM; Lobo, J.L.; Del Ser, J.Autoridad BCAM (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 ...
  • CURIE: a cellular automaton for concept drift detection 

    Lobo, J.L.; Del Ser, J.Autoridad BCAM; Osaba, E.; Bifet, A.; Herrera, F. (2021-11-01)
    Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as ...
  • LUNAR: Cellular automata for drifting data streams 

    Lobo, J.L.; Del Ser, J.Autoridad BCAM; Herrera, F. (2021-01-08)
    With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods ...
  • A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements 

    Shahriari, M.; Pardo, D.Autoridad BCAM; Picon, A.; Galdran, A.; Del Ser, J.Autoridad BCAM; Torres-Verdin, C. (2020-04)
    Borehole resistivity measurements are routinely employed to measure the electrical properties of rocks penetrated by a well and to quantify the hydrocarbon pore volume of a reservoir. Depending on the degree of geometrical ...
  • jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics 

    Del Ser, J.Autoridad BCAM; Benítez-Hidalgo, A.; J. Nebro, A.; García-Nieto, J.; Oregi, I. (2019-10-31)
    This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed ...
  • On-line Elastic Similarity Measures for time series 

    Oregui, I.; Pérez, A.Autoridad BCAM; Del Ser, J.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (2019-04)
    The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are ...
  • Intelligent Embedded Vision for Summarization of Multi-View Videos in IIoT 

    C. de Albuquerque, V.H.; Wook Baik, S.; Del Ser, J.Autoridad BCAM; Muhammad, K.; Hussain, T. (2019)
    Nowadays, video sensors are used on a large scale for various applications including security monitoring and smart transportation. However, the limited communication bandwidth and storage constraints make it challenging ...
  • On-Line Dynamic Time Warping for Streaming Time Series 

    Oregui, I.; Pérez, A.Autoridad BCAM; Del Ser, J.Autoridad BCAM; Lozano, J.A.Autoridad BCAM (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 ...
  • Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics 

    Landa-Torres, I.; Manjarres, D.; Bilbao, S.; Del Ser, J.Autoridad BCAM (2017-04)
    Robotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this ...
  • A Novel Grouping Harmony Search Algorithm for Clustering Problems 

    Landa-Torres, I.; Del Ser, J.Autoridad BCAM; Manjarres, D.; Gil-Lopez, S.; Salcedo-Sanz, S. (2017)
    The problem of partitioning a data set into disjoint groups or clusters of related items plays a key role in data analytics, in particular when the information retrieval becomes crucial for further data analysis. In this ...

More information