P. Larrañaga

Publications379
h-index55
Citations18,450
Highly Influential Citations754
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A review of feature selection techniques in bioinformatics
TLDR
A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
TLDR
This paper presents crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation.
A survey on multi‐output regression
TLDR
This study provides a survey on state‐of‐the‐art multi‐output regression methods, that are categorized as problem transformation and algorithm adaptation methods, and presents the mostly used performance evaluation measures, publicly available data sets for multi-output regression real‐world problems, as well as open‐source software frameworks.
Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters
TLDR
This work tackles the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures, by assuming an ordering between the nodes of the network structures.
An empirical comparison of four initialization methods for the K-Means algorithm
New insights into the classification and nomenclature of cortical GABAergic interneurons
TLDR
A possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria is described.
Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks
Bayesian Chain Classifiers for Multidimensional Classification
TLDR
This work introduces a method for chaining binary Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multidimensional classification and shows that this approach outperforms other state-of-the-art methods.
Estimation of Distribution Algorithms
TLDR
This work approaches the problem of partial abductive inference in Bayesian networks by means of Estimation of Distribution Algorithms, and an empirical comparison between the results obtained by Genetic Algorithm and Estimating of DistributionAlgorithms is carried out.
Machine learning in bioinformatics
TLDR
Modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization, are presented.
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