A comparison of PSO and GA approaches for gene selection and classification of microarray data

By José García-Nieto, Enrique Alba, Laetitia Vermeulen-Jourdan, El-Ghazali Talbi. 2007

25 citations recorded by the source corpus.

Feature selection for gene expression analysis in cancer prediction often uses wrapper classification methods to discriminate a type of tumor, to reduce the number of genes to investigate in case of a new patient. By creating clusters a big reduction of the number of considered genes and an improvement of the classification accuracy can be finally achieved. The definition of the feature selection problem is this: given a set of features F = {f1, ..., fi, ..., fn}, find a subset F ′ ⊆ F that maximizes a scoring function Θ : Γ → G such that F ′ = argmaxG⊂Γ{Θ(G)}, (1)

Subfields: Molecular Biology

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In Molecular Biology

1. Alper Ekrem Murat
2. Alper Ünler

Citations in the corpus

• A discrete particle swarm optimization method for feature selection in binary classification problems (2010, methodology): “García-Nieto et al. (2007) propose a hybrid method that combines PSO with support vector machine (SVM) approach as a wrapper method.”