• Publications
  • Influence
A review of feature selection techniques in bioinformatics
Feature selection techniques have become an apparent need in many bioinformatics applications. Expand
  • 3,842
  • 146
  • PDF
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
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
  • 723
  • 66
  • PDF
Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters
We present a new approach to structure learning in the field of Bayesian networks. Expand
  • 373
  • 27
  • PDF
Estimation of Distribution Algorithms
Estimation of Distribution Algorithms for Partial Abductive Inference in Bayesian Networks . Expand
  • 973
  • 26
New insights into the classification and nomenclature of cortical GABAergic interneurons
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 GABAergicExpand
  • 568
  • 24
  • PDF
A survey on multi‐output regression
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
  • 222
  • 23
  • PDF
An empirical comparison of four initialization methods for the K-Means algorithm
We aim to compare empirically four initialization methods for the K-Means algorithm: random, Forgy, MacQueen and Kaufman. Expand
  • 753
  • 20
Bayesian Chain Classifiers for Multidimensional Classification
We introduce a method for chaining binary Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multidimensional classification. Expand
  • 113
  • 18
  • PDF
Machine learning in bioinformatics
This article reviews machine learning methods for bioinformatics, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery. Expand
  • 604
  • 17
  • PDF