Machine Learning

An adaptive neuroevolution-basedhyperheuristic

Etor A.; Ceberio J.; Pérez A.; Irurozki E. (The Genetic and Evolutionary Computation Conference, 2020-07-08)

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 ...

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 ...

Soft information for localization-of-things

Conti A.; Mazuelas S.; Bartoletti S.; Lindsey W.C; Win M. (Proceeding of the IEEE, 2019-11-01)

Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such ...

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 ...

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 ...

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 ...

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 ...

Sentiment analysis with genetically evolved Gaussian kernels

Roman I.; Santana R.; Mendiburu A.; Lozano J.A. (GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, 2019)

Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. ...

Optimal multi-impulse space rendezvous considering limited impulse using a discretized Lambert problem combined with evolutionary algorithms

Shirazi A.; Ceberio J.; Lozano J.A. (8th European Conference for Aeronautics and Space Sciences, 2019-07-01)

In this paper, a direct approach is presented to tackle the multi-impulse rendezvous problem considering the impulse limit. Particularly, the standard Lambert problem is extended toward several consequential orbit transfers ...