DocumentCode
271764
Title
Decision support in attribute selection with machine learning approach
Author
Arbex, Wagner ; Conde de Oliveira, Fabrizzio ; Fonseca e Silva, Fabyano ; Varona, Luis ; Barbosa da Silva, Marcos ViniÌcius Gualberto ; da Silva Verneque, Rui ; Hasenclever Borges, Carlos Cristiano
Author_Institution
Brazilian Agric. Res. Corp. - Embrapa, Juiz de Fora, Brazil
fYear
2014
fDate
18-21 June 2014
Firstpage
1
Lastpage
5
Abstract
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.
Keywords
biology computing; decision support systems; learning (artificial intelligence); regression analysis; support vector machines; PUK; Pearson VII Universal Kernel; SNP marker; SVR; attribute selection; computational intelligence; continuous variable; decision support; machine learning approach; phenotype characterization; single nucleotide polymorphism marker; statistical tools; support vector regression; Accuracy; Dairy products; Genetic algorithms; Genetics; Kernel; Standards; Support vector machines; SVR; attribute selection; computational modeling; decision support; machine learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Systems and Technologies (CISTI), 2014 9th Iberian Conference on
Conference_Location
Barcelona
Type
conf
DOI
10.1109/CISTI.2014.6877002
Filename
6877002
Link To Document