Applying Bagging Techniques to the SA Tabu Miner Rule Induction Algorithm

Chorbev, Ivan; Andovska, Mirjana

doi:10.1007/978-3-642-10781-8_9

Cited by 1 publication

(1 citation statement)

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“…However, it should not be applied in the following cases: 1) with small data sets, or when the original sample is not representative of the population, 2) where there is noisy data because outliers add variability and 3) with strong dependency structures in the data (e.g. time series, spatial problems), because bootstrap sampling is based on the assumption of independence (Chorbev & Andovska ).…”

Section: Resultsmentioning

confidence: 99%

Assessment of bagging GBLUP for whole‐genome prediction of broiler chicken traits

Abdollahi-Arpanahi

Morota

Valente

et al. 2015

J Animal Breeding Genetics

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Bootstrap aggregation (bagging) is a resampling method known to produce more accurate predictions when predictors are unstable or when the number of markers is much larger than sample size, because of variance reduction capabilities. The purpose of this study was to compare genomic best linear unbiased prediction (GBLUP) with bootstrap aggregated sampling GBLUP (Bagged GBLUP, or BGBLUP) in terms of prediction accuracy. We used a 600 K Affymetrix platform with 1351 birds genotyped and phenotyped for three traits in broiler chickens; body weight, ultrasound measurement of breast muscle and hen house egg production. The predictive performance of GBLUP versus BGBLUP was evaluated in different scenarios consisting of including or excluding the TOP 20 markers from a standard genome-wide association study (GWAS) as fixed effects in the GBLUP model, and varying training sample sizes and allelic frequency bins. Predictive performance was assessed via five replications of a threefold cross-validation using the correlation between observed and predicted values, and prediction mean-squared error. GBLUP overfitted the training set data, and BGBLUP delivered a better predictive ability in testing sets. Treating the TOP 20 markers from the GWAS into the model as fixed effects improved prediction accuracy and added advantages to BGBLUP over GBLUP. The performance of GBLUP and BGBLUP at different allele frequency bins and training sample sizes was similar. In general, results of this study confirm that BGBLUP can be valuable for enhancing genome-enabled prediction of complex traits.

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Section: Resultsmentioning

confidence: 99%