The ROC curve is one of the most common statistical tools useful to assess classifier performance. The selection of the best classifier when ROC curves intersect is quite challenging. A novel approach for model comparisons when ROC curves show intersections is proposed. In particular, the relationship between ROC orderings and stochastic dominance is investigated in a theoretical framework and ...

-Receiver Operating Characteristic (ROC) curves are used to describe and compare the accuracy of diagnostic test or the ability of a continuous biomarker in discriminating between the subjects into healthy or diseased cases in medical field. The most familiar form of ROC curve is Bi-normal (Gaussian) ROC curve model, which assumes that the test scores or a monotone transformation of the test sc...

The receiver operating characteristic (ROC) curve is used for classification between two populations. Usually two independent random samples from the populations with absolutely continuous cumulative distribution functions (CDF) F and G are considered. Then the ROC curve is defined as ROC(t) = 1−G(F−1(1− t)) for 0 < t < 1 if the inverse F−1 exists. Classical ROC curve estimator is based on empi...

Effect of taurine on toxicity of aluminum in rats Yen-Hung Yeh *, Ya-Ting Lee , Hung-Sheng Hsieh , Deng-Fwu Hwang d Department of Nutrition and Health Science, Toko University, Chia-Yi, Taiwan, ROC Department of Beauty Science, Chienkuo Technology University, Changhua, Taiwan, ROC Department of Health and Nutrition Biotechnology, Asia University, Taichung, Taiwan, ROC Department of Food Science...

In this chapter we give an introduction to ROC (‘receiver operating characteristics’) analysis and its applications to data mining. We argue that ROC analysis provides decision support for data mining in several ways. For model selection, ROC analysis establishes a method to determine the optimal model once the operating characteristics for the model deployment context are known. We also show h...

Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls w...

1Institute of Molecular Medicine, National Cheng Kung University, Tainan, Taiwan, ROC 2Center of Infectious Disease and Signaling Research, National Cheng Kung University, Tainan, Taiwan, ROC 3Advanced Optoelectronic Technology Center, National Cheng Kung University, Tainan, Taiwan, ROC 4Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung, Taiwan, ...

Guaranteed cost control for uncertain non-linear systems with timevarying delays using T-S fuzzy model Yi-You Hou a; Teh-Lu Liao b; Jun-Juh Yan c; Chang-Hua Lien d a Industrial Technology Research Institute, Medical Electronics and Device Technology Centre, Hsin Chu, Taiwan, ROC b Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan, ROC c Department of Computer and...

The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the cu...