Discrimination

Superiority of artificial neural networks for a genetic classification procedure

I. C. Sant’Anna, Tomaz, R. S., Silva, G. N., Nascimento, M., Bhering, L. L., and Cruz, C. D., Superiority of artificial neural networks for a genetic classification procedure, vol. 14, pp. 9898-9906, 2015.

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, these methods have proven to be inefficient.

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