The performance of a classifier can be improved by abstaining on uncertain instance classifications. The transformation from the original Receiver Operator Characteristic (ROC) curve to the curve obtained by abstention is provided. We include proofs on dominance of this new ROC curve to aid classifier selection and to show the effectiveness of the approach. For specific cost and class distribut...

Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The...

We address the problem of comparing the performance of classifiers. In this paper we study techniques for generating and evaluating confidence bands on ROC curves. Historically this has been done using one-dimensional confidence intervals by freezing one variable—the false-positive rate, or threshold on the classification scoring function. We adapt two prior methods and introduce a new radial s...

In this paper we review the Receiver Operating Characteristic ROC curve and the test statistic in relation to the analysis of a confusion matrix We then show how these two methods are related and propose an extension to the ROC curve so that it shows contours of values These contours can be used to provide further insight into the appropriate setting of the decision threshold for a particular a...

Receiver operating characteristic (ROC) curves are useful statistical tools for medical diagnostic testing. It has been proved its capability to assess diagnostic marker’s ability to distinguish between healthy and diseased subjects and to compare different diagnostic markers. In this paper we introduce non parametric ROC summary statistics to assess a ROC curve across the entire range of FPFs ...

Declaration This dissertation is submitted to the University of Bristol in accordance with the requirements of the degree of Bachelor of Science in the Faculty of Engineering. It has not been submitted for any other degree or diploma of any examining body. Except where specifically acknowledged, it is all the work of the Author. 3 ABSTRACT Machine Learning applications require learning algorith...

Introduction Classification models and in particular binary classification models are ubiquitous in many branches of science and business. Consider, for example, classification models in bioinformatics that classify catalytic protein structures as being in an active or inactive conformation. As an example from the field of medical informatics we might consider a classification model that, given...

هدف: پژوهش حاضر با هدف تحلیل و بررسی پیامدهای شبکه‌سازی سیاسی در نظام سیاست‌گذاری سازمان‌های دولتی استان لرستان انجام پذیرفت.طراحی/ روش‌­شناسی/ رویکرد : دارای رویکردی آمیخته است که از نظر هدف، کاربردی حیث ماهیت روش، توصیفی پیمایشی است. مشارکت‌کنندگان را مدیران تشکیل می­‌دهند استفاده اصل کفایت نظری روش نمونه­‌گیری هدفمند 16 نفر آنان به عنوان اعضای نمونه انتخاب شدند. بخش کیفی برای گردآوری داده مص...

In this paper we investigate methods to detect and repair concavities in ROC curves by manipulating model predictions. The basic idea is that, if a point or a set of points lies below the line spanned by two other points in ROC space, we can use this information to repair the concavity. This effectively builds a hybrid model combining the two better models with an inversion of the poorer models...