Browsing by Author "Pérez A."
Now showing items 114 of 14

Supervised nonparametric discretization based on Kernel density estimation
Flores J. L.; Calvo B.; Pérez A. (Pattern Recognition Letters, 20191219)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 ... 
Online Elastic Similarity Measures for time series
Oregui I.; Pérez A.; Del Ser J.; Lozano J.A. (Pattern Recognition, 201904)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 ... 
OnLine 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, 201709)Dynamic Time Warping is a wellknown measure of dissimilarity between time series. Due to its flexibility to deal with nonlinear distortions along the time axis, this measure has been widely utilized in machine learning ... 
Natureinspired 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), 201707)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, 202007)The Quadratic Assignment Problem (QAP) is a wellknown permutationbased combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NPhard ... 
An efficient Kmeans 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 Kmeans clustering for Massive Data
Capo M.; Pérez A.; Lozano J.A. (KnowledgeBased Systems, 20160628)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 Kmeans clustering for Massive Data
Capo M.; Pérez A.; Lozano J.A. (KnowledgeBased Systems, 20170201)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 korder decomposable models
Pérez A.; Inza I.; Lozano J.A. (International Journal of Approximate Reasoning, 20160101)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 korder decomposable models
Pérez A.; Inza I.; Lozano J.A. (International Journal of Approximate Reasoning, 201607)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 EMbased Solution
BeñaranMuñoz I.; HernándezGonzález J.; Pérez A. (Conference of the Spanish Association for Artificial Intelligence, 20180927)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, 201806)In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a testbed 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, 201907)The Quadratic Assignment Problem (QAP) is a specially challenging permutationbased nphard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ... 
An adaptive neuroevolutionbased hyperheuristic
Arza E.; Ceberio J.; Pérez A.; Irurozki E. (The Genetic and Evolutionary Computation Conference, 2020)According to the NoFreeLunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problemspecific knowledge usually outperform more generic ...