منابع مشابه
Conditional Probabilities, Relative Operating Characteristics, and Relative Operating Levels
The relative operating characteristic (ROC) curve is a highly flexible method for representing the quality of dichotomous, categorical, continuous, and probabilistic forecasts. The method is based on ratios that measure the proportions of events and nonevents for which warnings were provided. These ratios provide estimates of the probabilities that an event will be forewarned and that an incorr...
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Supervised learning algorithms perform common tasks including classification, ranking, scoring, and probability estimation. We investigate how scoring information, often produced by these models, is utilized by an evaluation measure. The ROC curve represents a visualization of the ranking performance of classifiers. However, they ignore the scores which can be quite informative. While this igno...
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Genetic programming (GP) can automatically fuse given classifiers of diverse types to produce a combined classifier whose Receiver Operating Characteristics (ROC) are better than [Scott et al.1998b]’s “Maximum Realisable Receiver Operating Characteristics” (MRROC). I.e. better than their convex hull. This is demonstrated on a satellite image processing bench mark using Naive Bayes, Decision Tre...
متن کاملEvolving Receiver Operating Characteristics for Data Fusion
It has been suggested that the \Maximum Realisable Receiver Operating Characteristics" for a combination of classiiers is the convex hull of their individual ROCs Scott et al., 1998]. As expected in at least some cases better ROCs can be produced. We show genetic programming (GP) can automatically produce a combination of classi-ers whose ROC is better than the convex hull of the supplied class...
متن کاملFeature Weighted SVMs Using Receiver Operating Characteristics
Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used to map the input space into a high dimensional feature space. However, it can perform rather poorly when there are too many dimensions (e.g. for gene expression data) or when there is a lot of noise. In this paper, we ...
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ژورنال
عنوان ژورنال: Japanese Journal of Radiological Technology
سال: 1990
ISSN: 0369-4305,1881-4883
DOI: 10.6009/jjrt.kj00003321759