.png){kind=icon}
返回
Optimal ROC-Based Classification and Performance Analysis under Bayesian Uncertainty Models
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2015.2465966.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Popular tools to evaluate classifier performance are the false positive rate (FPR), true positive rate (TPR), receiver operator characteristic (ROC) curve, and area under the curve (AUC). Typically, these quantities are estimated from training data using simple resampling and counting methods, which have been shown to perform poorly when the sample size is small, as is typical in many applications. This work takes a model-based approach in classifier training and performance analysis, where we assume the true population densities are members of an uncertainty class of distributions. Given a prior over the uncertainty class and data, we form a posterior and derive optimal mean-squared-error (MSE) FPR and TPR estimators, as well as the sample-conditioned MSE performance of these estimators. The theory also naturally leads to optimal ROC and AUC estimators. Finally, we develop a Neyman-Pearson-based approach to optimal classifier design, which maximizes the estimated TPR for a given estimated FPR. These tools are optimal over the uncertainty class of distributions given the sample, and are available in closed form or can be easily approximated for many models. Applications are demonstrated on both synthetic and real genomic data. MATLAB code and simulations results are available in the online supplementary material.
Keyword:
Classification
error estimation
Bayesian estimation
receiver operating characteristic
area under the curve
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
3.4
论文数:
3.3K
被引数:
6.4K
机构
引用论文
分享收藏Optimal classifiers with minimum expected error within a Bayesian framework-Part I: Discrete and Gaussian models.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏A simple generalisation of the area under the ROC curve for multiple class classification problems多类分类问题的ROC曲线下面积的简单概括MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏
查看更多内容