Now showing items 1-14 of 14

    • Supervised non-parametric discretization based on Kernel density estimation 

      Flores J. L.; Calvo B.; Pérez A. (Pattern Recognition Letters, 2019-12-19)
      Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main ...
    • On-line Elastic Similarity Measures for time series 

      Oregui I.; Pérez A.; Del Ser J.; Lozano J.A. (Pattern Recognition, 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 ...
    • On-Line Dynamic Time Warping for Streaming Time Series 

      Oregui 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 ...
    • 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 ...
    • Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem 

      Arza E.; Pérez A.; Irurozki E.; Ceberio J. (Swarm and Evolutionary Computation, 2020-07)
      The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard ...
    • An efficient K-means clustering algorithm for tall data 

      Capo M.; Pérez A.; Lozano J.A. (DATA MINING AND KNOWLEDGE DISCOVERY, 2020)
      The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial ...
    • An efficient approximation to the K-means clustering for Massive Data 

      Capo M.; Pérez A.; Lozano J.A. (Knowledge-Based Systems, 2016-06-28)
      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 ...
    • 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, 2016-01-01)
      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 ...
    • 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 ...
    • 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 ...
    • 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 ...
    • 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 ...
    • An adaptive neuroevolution-based hyperheuristic 

      Arza E.; Ceberio J.; Pérez A.; Irurozki E. (The Genetic and Evolutionary Computation Conference, 2020)
      According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic ...