P. Larrañaga

Publications
Influence

A review of feature selection techniques in bioinformatics

Y. Saeys, I. Inza, P. Larrañaga
Computer Science, Medicine
Bioinform.
10 September 2007

Feature selection techniques have become an apparent need in many bioinformatics applications. Expand

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Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

P. Larrañaga, Cindy M. H. Kuijpers, R. Murga, I. Inza, S. Dizdarevic
Computer Science
Artificial Intelligence Review
1 April 1999

We present 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. Expand

Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

P. Larrañaga, M. Poza, Y. Yurramendi, R. Murga, Cindy M. H. Kuijpers
Computer Science
IEEE Trans. Pattern Anal. Mach. Intell.
1 September 1996

We present a new approach to structure learning in the field of Bayesian networks. Expand

Estimation of Distribution Algorithms

P. Larrañaga, J. Lozano
Computer Science
Genetic Algorithms and Evolutionary Computation
2002

Estimation of Distribution Algorithms for Partial Abductive Inference in Bayesian Networks . Expand

New insights into the classification and nomenclature of cortical GABAergic interneurons

J. DeFelipe, Pedro L. López-Cruz, +39 authors G. Ascoli
Biology, Medicine
Nature Reviews Neuroscience
1 March 2013

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic… Expand

A survey on multi‐output regression

H. Borchani, Gherardo Varando, C. Bielza, P. Larrañaga
Computer Science
Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
1 September 2015

A survey on state‐of-the-art multi-output regression methods, that are categorized as problem transformation and algorithm adaptation methods, as well as open‐source software frameworks. Expand

An empirical comparison of four initialization methods for the K-Means algorithm

J. M. Peña, J. Lozano, P. Larrañaga
Mathematics, Computer Science
Pattern Recognit. Lett.
1 October 1999

We aim to compare empirically four initialization methods for the K-Means algorithm: random, Forgy, MacQueen and Kaufman. Expand

Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks

P. Larrañaga, R. Etxeberria, J. Lozano, J. M. Peña
Computer Science
2000

Bayesian Chain Classifiers for Multidimensional Classification

Julio H. Zaragoza, L. Sucar, E. Morales, C. Bielza, P. Larrañaga
Mathematics, Computer Science
IJCAI
16 July 2011

We introduce a method for chaining binary Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multidimensional classification. Expand

Machine learning in bioinformatics

P. Larrañaga, B. Calvo, +8 authors V. Robles
Computer Science, Medicine
Briefings Bioinform.
1 March 2006

This article reviews machine learning methods for bioinformatics, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery. Expand

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