2015
DOI: 10.4238/2015.august.19.24
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Superiority of artificial neural networks for a genetic classification procedure

Abstract: ABSTRACT. The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations,… Show more

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Cited by 26 publications
(28 citation statements)
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“…In addition, due to their non-linear structure (Haykin, 2009) ANNs can capture more complex features of data sets and do not require detailed information about the process to be modeled due to its self-learning (Nascimento et al, 2013;Silva et al, 2014;Sant'Anna et al, 2015).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In addition, due to their non-linear structure (Haykin, 2009) ANNs can capture more complex features of data sets and do not require detailed information about the process to be modeled due to its self-learning (Nascimento et al, 2013;Silva et al, 2014;Sant'Anna et al, 2015).…”
Section: Resultsmentioning
confidence: 99%
“…Subsequently, the simulated genotypes are used to train and validate neural networks. Thus, by the training of ANNs, the identifying the stable genotypes is not only executed based on the studied genotypes, but for a large collection of simulated genotypes according to predefined groups (Nascimento et al, 2013;Silva et al, 2014;Sant'Anna et al, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Silva et al (2014Silva et al ( , 2016 concluded that ANNs are efficient in predicting values and genetic gain in simulated trials under randomized block design. Regarding the classification studies, Sant'Anna et al (2015) showed that neural networks had results superior to those obtained by discriminant analysis in the classification of simulated populations. Table 2.…”
Section: Resultsmentioning
confidence: 99%
“…For the network training (obtainment of weights), the set of observations was divided in two parts: training and validation, as commonly adopted in the literature (Silva et al, 2014(Silva et al, , 2016Sant'Anna et al, 2015). The first, denoted by training set, consisted of 160 individuals taken at random.…”
Section: Phenotypingmentioning
confidence: 99%
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