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| 1 | On Splitting Training and Validation Set:A Comparative Study of Cross-Validation,Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning显示文摘Model validation is the most important part of building a supervised model.For building a model with good generalization performance one must have a sensible data splitting strategy,and this is crucial for model validation.In this study,we con-ducted a comparative study on various reported data splitting methods.The MixSim model was employed to generate nine simulated datasets with different probabilities of mis-classification and variable sample sizes.Then partial least squares for discriminant analysis and support vector machines for classification were applied to these datasets.Data 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 sets.The 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 construction.The results showed that the size of the data is the deciding factor for the qualities of the generalization performance estimated from the validation set.We 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 datasets.Such 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 used.We 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 performance.We also found that systematic sampling method such as K-S and SPXY generally had very poor estimation of the model performance,most likely due to the fact that they are designed to take the most representative samples first and thus left a rather poorly representative sample set for model performance estimation. | Yun Xu Royston Goodacre | 2018 | Journal of Analysis and Testing2018，2，3： | 8 |
| 2 | 较小样本动态声发射信号多元统计分析技术显示文摘利用多传感信息集成系统,以两组平均年龄对应的受试对象往复运动过程中获取的动态声发射信号和角度信号为对象,研究了适用于较小样本的动态声发射信号多元统计分析技术。通过同步记录的角度信号,将往复运动分解为若干个独立运动周期和运动过程;利用累计概率分布,选取具备较显著差异的特征;结合多元统计技术,减小数据量,建立动态声发射信号的可视化模型,证实了使用较小样本声发射信号实现膝盖骨关节诊断的可行性。 | 陈宏志 芦永明 王丽娜 Lik-Kwan Shark John Goodacre | 2013 | 振动.测试与诊断2013，33，2： | 5 |
| 3 | Clinical complications in fixed prosthodontics 显示文摘 | Goodacre C J Bemal G Rungcharassaeng K | 2003 | J Prosthet Dent2003，90，1： | 1 |
| 4 | Rapid quantitative assessment of the adulteration of virgin olive oils with hazelnut oils using Raman spectroscopy and chemometrics显示文摘 | Lopez-Diez E C Bianchi G Goodacre R | 2003 | Journal of Agricultural and Food Chemistry2003，51，21： | 1 |
| 5 | Making sense of the metabolome using evolutionary computation:seeing the wood with the trees显示文摘 | | 2005 | J Exp Bot2005，56，410： | 1 |
| 6 | Clinical complication s in fixed prosthodontics显示文摘 | Goodacre CJ Bemal G Rungcharassaeng K | 2003 | J prosthet Dent2003，90，1： | 1 |
| 7 | Measurement of the clinical and cost-effectiveness of non-invasive diagnostic testing strategies for deep vein thrombosis 显示文摘 | Goodacre S Sampson F Stevenson M | 2006 | Health Technol Assess2006，10，15： | 1 |
| 8 | Metabolomics:Current technologies and future trends显示文摘 | Hollywood K Brison DR Goodacre R | 2006 | Proteomics2006，6，47： | 1 |
| 9 | Ftow-injection electrospray ionization mass spectrometry of crude cell extracts for highthroughput bacterial identification 显示文摘 | Vaidyanathan S Kell DB Goodacre R | 2002 | J Am Soc Mass Spectrom2002，13，2： | 1 |
| 10 | ABC of clinical electrocardiography:atrial arrhythmias显示文摘 | Goodacre S Irons R | 2002 | BMJ2002，324，7337： | 1 |
| 11 | The Long Run Share Price Performance of Malaysian Initial Public Offerings (IPOs)显示文摘 | AHMAD-ZALUKI N A K CAMPBELL A GOODACRE | 2007 | Journal of Business Finance & Accounting2007，34，12： | 1 |
| 12 | 显示文摘 | GOODACRE R | 2007 | Journal of Nutrition2007，137，1： | 1 |
| 13 | Tooth preparations forcomplete crowns:an art form based on scientific principles显示文摘 | Goodacre CJ Campagni WV Aquilino SA | 2001 | JProsthet Dent2001，85，4： | 1 |
| 14 | Comparison of contemporarytroponin assays with the novel biomarkers,heart fatty acid bindingprotein and copeptin, for the early confirmation or exclusion of my-ocardial infarction in patients presenting to the emergency depart-ment with chest pain显示文摘 | Collinson P Gaze D Goodacre S | 2014 | Heart2014，100，2： | 1 |
| 15 | An introduction to liquid chromatography-mass spectrometry instrumentation applied in plant metabolomic analyses显示文摘 | ALLWOOD J W GOODACRE R | 2010 | PhytochemicalAnalysis2010，21，1： | 1 |
| 16 | Prosthodontic considerations when using implants for orthodontic anchorage 显示文摘 | Goodacre CJ Brown DT Roberts WE | 1997 | J Prosthet Dent1997，77，： | 1 |
| 17 | HPLC Instrumentation Applied in Plant Metabolomic Analyses显示文摘 | ALLWOOD J W GOODACRE R | 2010 | Phytochemical Analysis2010，21，： | 1 |
| 18 | Noninvasive ventilation in a- cute cardiogenic pulmonary edema显示文摘 | Gray A Goodacre S David E N | 2008 | N Engl J Med2008，359，2： | 1 |
| 19 | Metabolic fingerprinting in disease diagno- sis: biomedical applications of infrared and Raman spectroscopy 显示文摘 | Ellis DI Goodacre R | 2006 | Analyst2006，131，8： | 1 |
| 20 | Clinical com- plications in fixed prosthodontics 显示文摘 | Goodacre C J Bernal G Rungcharassaeng K | 2003 | J Prosthet Dent2003，90，1： | 1 |
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