Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data

ORNELLA, L.; TAPIA, E.

COMPUTERS AND ELETRONICS IN AGRICULTURE

ELSEVIER SCI LTD

Lugar: NORTH-HOLLAND; Año: 2010 vol. 74 p. 250 - 257

0168-1699

The development of molecular techniques for genetic analysis has enabled great advances in cereal breeding.However, their usefulness in hybrid breeding, particularly in assigning new lines to heterotic groupspreviously established, still remains unsolved. In this work we evaluate the performance of several stateof-art multiclass classifiers onto three molecular marker datasets representing a broad spectrum of maizeheterotic patterns. Even though results are variable, they suggest supervised learning algorithms as avaluable complement to traditional breeding programs.