A comparison of random forests, boosting and support vector machines for genomic selection
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Ogutu, J.O., Piepho, H.-P. and Schulz-Streeck, T. 2011. A comparison of random forests, boosting and support vector machines for genomic selection. BMC Proceeding 5(Suppl 3):S11.
Permanent link to this item: http://hdl.handle.net/10568/3795
Genomic selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. Accurate prediction of genomic breeding values (GEBVs) presents a central challenge to contemporary plant and animal breeders. The existence of a wide array of marker-based approaches for predicting breeding values makes it essential to evaluate and compare their relative predictive performances to identify approaches able to accurately predict breeding values. We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs.