<b>Mateda-2.0</b>: A<i>MATLAB</i>Package for the Implementation and Analysis of Estimation of Distribution Algorithms

Santana, Roberto; Bielza, Concha; Larrañaga, Pedro; Lozano, José A.; Echegoyen, Carlos; Mendiburu, Alexander; Armañanzas, Rubén; Shakya, Siddartha

doi:10.18637/jss.v035.i07

Cited by 39 publications

(6 citation statements)

References 58 publications

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“…The scheme of Algorithm 2 corresponds to the algorithm introduced in [36]. It has been implemented in MATLAB using the MATEDA software [37], a highly modular implementation in which each EDA component (either added by the user or already included in the package) is implemented as an independent program.…”

Section: 3mentioning

confidence: 99%

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Astigarraga¹,

Arruti

Muguerza³

et al. 2016

Mathematical Problems in Engineering

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Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

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Section: 3mentioning

confidence: 99%

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Astigarraga¹,

Arruti

Muguerza³

et al. 2016

Mathematical Problems in Engineering

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“…This feature subset selection problem, with 39 variables, is addressed using a tree-based EDA as implemented with MATEDA. 44 EDAs have shown to be a good alternative for feature subset selection problems. 47 Only one run of Tree-EDA was used to compute the best set of features.…”

Section: Density Of a Solution Given The Class Valuementioning

confidence: 99%

Network measures for information extraction in evolutionary algorithms

Santana¹,

Armañanzas²,

Bielza³

et al. 2013

IJCIS

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Problem domain information extraction is a critical issue in many real-world optimization problems. Increasing the repertoire of techniques available in evolutionary algorithms with this purpose is fundamental for extending the applicability of these algorithms. In this paper we introduce a unifying information mining approach for evolutionary algorithms. Our proposal is based on a division of the stages where structural modelling of the variables interactions is applied. Particular topological characteristics induced from different stages of the modelling process are identified. Network theory is used to harvest problem structural information from the learned probabilistic graphical models (PGMs). We show how different statistical measures, previously studied for networks from different domains, can be applied to mine the graphical component of PGMs. We provide evidence that the computed measures can be employed for studying problem difficulty, classifying different problem instances and predicting the algorithm behavior.

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“…All the optimization algorithms (GA, UMDA, and Tree-EDA) are implemented using the MATEDA-2.0 software (Santana et al 2010c), a modular implementation of estimation of distribution algorithms programmed in Matlab (The Math Works 2007) that can be used to implement genetic and other classes of EAs. The computation of all network measures is implemented using the brain connectivity toolbox (Sporns 2002).…”

Section: Overview Of the Experimentsmentioning

confidence: 99%

Regularized logistic regression and multiobjective variable selection for classifying MEG data

2012

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This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

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Mateda-2.0: AMATLABPackage for the Implementation and Analysis of Estimation of Distribution Algorithms

Cited by 39 publications

References 58 publications

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Network measures for information extraction in evolutionary algorithms

Regularized logistic regression and multiobjective variable selection for classifying MEG data

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