A platform for the selection of genes in DNA microarraydata using evolutionary algorithms

Authors:
Miguel Rocha;Rui Mendes;Paulo Maia;Daniel Glez-Peña;Florentino Fdez-Riverola
Affiliations:
Universidade do Minho;Universidade do Minho;Universidade do Minho;University of Vigo;University of Vigo
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

Citing 7
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Abstract

This paper presents a flexible framework to the task of featureselection in classification of DNA microarray data. Theuser can select a number of filter methods in the preprocessingstage and choose from a wide set of classifiers (models and algorithms from WEKA [17] are available) and accuracy estimation methods. This approach implements wrapper methods, where Evolutionary Algorithms, with variable sized set based representations are used to reduce the number of attributes. Two case studies were used to validate the approach, with three distinct classifiers (1-nearest neighbour, decision trees, SVMs), a filter method based on discriminant fuzzy patterns and k-fold cross-validation to estimate the generalization error.