The paper considers non-parametric maximum likelihood estimation of the failure time distribution for interval censored data subject to misclassification. Such data can arise from two types of observation scheme; either where observations continue until the first positive test result or where tests continue regardless of the test results. In the former case, the misclassification probabilities ...

We present the notion of Ranking for evaluation of two-class classifiers. Ranking is based on using the ordering information contained in the output of a scoring model, rather than just setting a classification threshold. Using this ordering information, we can evaluate the model's performance with regard to complex goal functions, such as the cor rect identification of the k most likely and/or...

For the learning of Classification Tree, many researchers have used different splitting criteria, in which most commonly impurity-based criteria are: Gini index, Entropy function and Exponent-based index. By comparing Misclassification rates, none of the splitting criterion can be declared as providing best results in every situation. In this study, a new Mean Deviation based index has been pro...

Large margin linear classification methods have been successfully applied to many applications. For a linearly separable problem, it is known that under appropriate assumptions, the expected misclassification error of the computed “optimal hyperplane” approaches zero at a rate proportional to the inverse training sample size. This rate is usually characterized by the margin and the maximum norm...

This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specific diagnostic test) and misclassification costs (e.g. the cost of a false negative test). In fact, as in other professional areas, both diagnost...

Genetic epidemiology studies often adjust for numerous potential confounders, yet the influences of confounder misclassification and selection bias are rarely considered. We used simulated data to evaluate the effect of confounder misclassification and selection bias in a case-control study of incident myocardial infarction. We show that putative confounders traditionally included in genetic as...

We explore a novel approach to upper bound the misclassification error for problems with data comprising a small number of positive samples and a large number of negative samples. We assign the hinge-loss to upper bound the misclassification error of the positive examples and use the minimax risk to upper bound the misclassification error with respect to the worst case distribution that generat...

One of the challenges to implementing sensitivity analysis for exposure misclassification is the process of specifying the classification proportions (eg, sensitivity and specificity). The specification of these assignments is guided by three sources of information: estimates from validation studies, expert judgment, and numerical constraints given the data. The purpose of this teaching paper i...

Misclassification and measurement error are very common in studies of effects of exposures and are often ignored. The problem of exposure misclassification for interaction measures in the context of both multiplicative interaction (1–4) and additive interaction (5) has been discussed previously. Other investigators have discussed problems of outcome misclassification for a single exposure (6, 7...

PURPOSE To estimate the misclassification rate of self-reported visual disabilities in a hospital-based population with known visual impairment. METHODS Subjects (N=570) were recruited among patients aged 50 years and more and classified to three categories of visual impairment level. The questionnaire was administered to consenting patients through a telephone interview. Data collected from ...