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On Splitting Training and Validation Set:A Comparative Study of Cross-Validation,Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning

引用

Journal of analysis and Testing 2018年第3期2卷 249-262页

作者： Yun Xu Royston Goodacre School of Chemistry Manchester Institute of BiotechnologyThe University of ManchesterManchester M17DNUK Department of Biochemistry Institute of Integrative BiologyUniversity of LiverpoolBiosciences BuildingCrown StreetLiverpool L697ZBUK

Model validation is the most important part of building a supervised *** building a model with good generalization performance one must have a sensible data splitting strategy,and this is crucial for model *** this study,we con-ducted a comparative study on various reported data splitting *** MixSim model was employed to generate nine simulated datasets with different probabilities of mis-classification and variable sample *** partial least squares for discriminant analysis and support vector machines for classification were applied to these *** splitting methods tested included variants of cross-validation,bootstrapping,bootstrapped Latin partition,Kennard-Stone algorithm(K-S)and sample set partitioning based on joint X-Y distances algorithm(SPXY).These methods were employed to split the data into training and validation *** estimated generalization performances from the validation sets were then compared with the ones obtained from the blind test sets which were generated from the same distribution but were unseen by the train-ing/validation procedure used in model *** results showed that the size of the data is the deciding factor for the qualities of the generalization performance estimated from the validation *** found that there was a significant gap between the performance estimated from the validation set and the one from the test set for the all the data splitting methods employed on small *** disparity decreased when more samples were available for training/validation,and this is because the models were then moving towards approximations of the central limit theory for the simulated datasets *** also found that having too many or too few samples in the training set had a negative effect on the estimated model performance,suggesting that it is necessary to have a good balance between the sizes of training set and validation set to have a reliable estimation of model *** a

关键词： Cross-validation Bootstrapping Bootstrapped Latin partition Kennard-Stone algorithm SPXY Model selection Model validation partial least squares for discriminant analysis Support vector machines

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