Recent Submissions

  • Migration in Multi-Population Differential Evolution for Many Objective Optimization 

    Rakshit P.; Chowdhury A.; Konar A.; Nagar A.K. (2020 IEEE Congress on Evolutionary Computation (CEC), 2020)
    The paper proposes a novel extension of many objective optimization using differential evolution (MaODE). MaODE solves a many objective optimization (MaOO) problem by parallel optimization of individual objectives. MaODE ...
  • Q-Learning Induced Artificial Bee Colony for Noisy Optimization 

    Rakshit P.; Konar A.; Nagar A.K. (2020 IEEE Congress on Evolutionary Computation (CEC), 2020)
    The paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution ...
  • A Machine Learning Approach to Predict Healthcare Cost of Breast Cancer Patients 

    Rakshit P.; Zaballa-Larumbe O.; Pérez A.; Gomez-Inhiesto E.; Acaiturri-Ayesta M.T.; Lozano J.A. (Scientific Reports, Springer Nature, 2021)
    This paper presents a novel machine learning approach to per- form an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: i) in ...
  • Journey to the center of the linear ordering problem 

    Hernando L.; Mendiburu A.; Lozano J.A. (GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020-06)
    A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the ...
  • Mutual information based feature subset selection in multivariate time series classification 

    Ircio J.; Lojo A.; Mori U.; Lozano J.A. (Pattern Recognition, 2020)
    This paper deals with supervised classification of multivariate time se- ries. In particular, the goal is to propose a filter method to select a subset of time series. Consequently, we adopt the framework proposed by Brown ...
  • in-depth analysis of SVM kernel learning and its components 

    Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (Neural Computing and Applications, 2020)
    The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs ...
  • General supervision via probabilistic transformations 

    Mazuelas S.; Perez A. (Proceedings of the 24th European Conference on Artificial Intelligence-ECAI, 2020-08-01)
    Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ...
  • Application of machine learning techniques to weather forecasting 

    Rozas Larraondo P. (2018-10-24)
    Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the ...
  • Theoretical and Methodological Advances in Semi-supervised Learning and the Class-Imbalance Problem 

    Ortigosa-Hernandez J. (2018-11-30)
    his paper focuses on the theoretical and practical generalization of two known and challenging situations from the field of machine learning to classification problems in which the assumption of having a single binary class ...
  • 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 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 ...
  • Evolving Gaussian Process Kernels for Translation Editing Effort Estimation 

    Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (Learning and Intelligent Optimization, 2019)
    In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, ...
  • Bayesian Optimization Approaches for Massively Multi-modal Problems 

    Roman I.; Mendiburu A.; Santana R.; Lozano J.A. (Learning and Intelligent Optimization, 2019)
    The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the op- timization process to local optima. While evolutionary algorithms have been ...
  • Advances on Time Series Analysis using Elastic Measures of Similarity 

    Oregui I. (2020-07-23)
    A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series ...
  • Optimization of deep learning precipitation models using categorical binary metrics 

    Larraondo P.R.; Renzullo L.J.; Van Dijk A.I.J.M.; Inza I.; Lozano J.A. (Journal of Advances in Modeling Earth Systems, 2020)
    This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm ...
  • 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 ...
  • 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 ...
  • An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization 

    Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (IEEE Access, 2019)
    Bayesian Optimization has been widely used along with Gaussian Processes for solving expensive-to-evaluate black-box optimization problems. Overall, this approach has shown good results, and particularly for parameter ...
  • Hybrid Heuristics for the Linear Ordering Problem 

    Garcia E.; Ceberio J.; Lozano J.A. (2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019)
    The linear ordering problem (LOP) is one of the classical NP-Hard combinatorial optimization problems. Motivated by the difficulty of solving it up to optimality, in recent decades a great number of heuristic and meta-heuristic ...

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