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Decision in Attribute Selection with Machine Learning Approach

This paper proposes a method to simultaneously select the most relevant single nucleotide polymorphisms (SNPs) markers — the attributes — for the characterization of any measurable phenotype described by a continuous variable using support vector regression (SVR) with Pearson VII Universal Kernel (PUK). The proposed study is multiattribute towards considering several markers simultaneously to explain the phenotype and is based jointly on a statistical tools, machine learning and computational intelligence.

Author(s):

Wagner Arbex    
Brazilian Agricultural Research Corporation - Embrapa
Brazil

Fabrízzio Oliveira    
Federal University of Juiz de Fora - UFJF
Brazil

Fabyano Silva    
Federal University of Viçosa - UFV
Brazil

Luis Varona    
University of Zaragoza - UNIZAR
Spain

Marcos Vinícius Silva    
Brazilian Agricultural Research Corporation - Embrapa
Brazil

Rui Verneque    
Brazilian Agricultural Research Corporation - Embrapa
Brazil

Carlos Cristiano Borges    
Federal University of Juiz de Fora - UFJF
Brazil