Predicting sample size required for classification performance

BMC Medical Informatics and Decision Making

Research Article

Rosa L Figueroa¹  Qing Zeng-Treitler²  Sasikiran Kandula²  Long H Ngo³ 

[1] Dep. Ing. Eléctrica, Facultad de Ingeniería, Universidad de Concepción, Concepción, Chile;Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA;Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA;

关键词: Root Mean Square Error;    Learning Curve;    Active Learning;    Mean Absolute Error;    Annotate Data;   

DOI  :  10.1186/1472-6947-12-8

 received in 2011-06-30, accepted in 2012-02-15,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundSupervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.MethodsWe designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method.ResultsA total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05).ConclusionsThis paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.

【 授权许可】

Unknown   
© Figueroa et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.