Ensemble supervised learning for genomic selection To meet the worldu0027s growing food and nutrition demands, agricultural breeders need to grow crops with improved phenotypes and create varieties that allow increased production. Genomic selection enables the breeders to select individuals with improved phenotypes even before growing them. Existing genomic selection is made mostly through statistical methods that do not accurately predict complex nonlinear traits. Deep learning and other machine learning methods have been applied, but most of the deep learning methods are not specifically designed for genomic selection. Also, there has been relatively little comparison between different machine learning methods. We propose three ensemble learning methods: i) ensemble support vector regression; ii) ensemble deep convolutional neural networks and iii) random forests; to predict phenotype with high accuracy. We also propose a feature selection strategy that identifies important markers and contributes to improved phenotype prediction. The proposed marker selection strategy is independent of machine learning methods; thus, the markers that are selected remain the same when the machine learning model is changed. We employed our methods to Iranian wheat landraces. The result shows that ensemble learning methods are better than the single machine learning methods with the lowest PCC 0.339 for plant height and the highest PCC 0.747 for grain length. Our models are also robust as they rank both top twenty and bottom twenty individuals well with nDCG20 ranges from 0.188 to 0.712.

Random rotation ensembles In machine learning, ensemble methods combine the predictions of multiple base learners to construct more accurate aggregate predictions. Established supervised learning algorithms inject randomness into the construction of the individual base learners in an effort to promote diversity within the resulting ensembles. An undesirable side effect of this approach is that it generally also reduces the accuracy of the base learners. In this paper, we introduce a method that is simple to implement yet general and effective in improving ensemble diversity with only modest impact on the accuracy of the individual base learners. By randomly rotating the feature space prior to inducing the base learners, we achieve favorable aggregate predictions on standard data sets compared to state of the art ensemble methods, most notably for treebased ensembles, which are particularly sensitive to rotation.