Resumen

DUSSAUT, J. S.; PONZONI, I.; OLIVERA, A. C.  y  VIDAL, P. J.. Multiobjective Evolutionary Algorithms applied to Feature Selection in Microarrays Cancer Data. Entre Ciencia e Ingenieria [online]. 2020, vol.14, n.28, pp.40-45.  Epub 16-Abr-2021. ISSN 1909-8367.  https://doi.org/10.31908/19098367.2014.

Microarray analysis of gene expression is a current topic for diagnosing and classification of human cancer. A gene expression data microarray consists of an array of thousands of features of which most are irrelevant for classifying patterns of gene expressions. Choosing a minimal subset of features for classification is a difficult task. In this work, a comparison is made between two multi-objective evolutionary algorithms applied to sets of gene expressions popular in the literature (lymphoma, leukemia, and colon). In order to remove the strongly correlated characteristics, a pre-processing stage is performed. An extensive and detailed analysis of the results obtained for the selected multi-objective algorithms is shown.

Palabras clave : Cancer Microarrays; Feature Selection; Gene Expression; Multiobjective Evolutionary Algorithms.

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