When product quality characteristics are evaluated and assigned to exclusive categories, measurement errors (misclassification of products) always exist unless a perfect measurement system is used to identify the categories. In run-to-run (R2R) process control, a categorical controller has been developed for process adjustments with categorical variables. However, if process outputs are misclas...

In most epidemiological surveys, there will be some errors of measurement or classification of exposure. For example, for a binary exposure variable, some exposed subjects may be classified as non-exposed, and some nonexposed subjects may be classified as exposed. Non-differential misclassification of exposure is present if, irrespective of disease, all exposed and non-exposed subjects have the...

Classification margin is commonly used for describing the classification capability of a committee of classifiers. In this paper, we study the relation between classification margin and misclassification error, focusing on exploring useful information about misclassification error from the known classification margin. We propose a max–min type bound concerning the minimal misclassification rate...

We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighted message passing algorithm to reconstruct labels for SBM based on the minimu...

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and instance acquisition effort, considering all the data attributes accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class...

Background. Pattern identification (PI) is the basic system for diagnosis of patients in traditional Korean medicine (TKM). The purpose of this study was to identify misclassification objects in discriminant model of PI for improving the classification accuracy of PI for stroke. Methods. The study included 3306 patients with stroke who were admitted to 15 TKM hospitals from June 2006 to Decembe...

The problem of misclassification is common in epidemiological and clinical research. In some cases, misclassification may be incurred when measuring both exposure and outcome variables. It is well known that validity of analytic results (e.g. point and confidence interval estimates for odds ratios of interest) can be forfeited when no correction effort is made. Therefore, valid and accessible m...

BACKGROUND Misclassification bias is present in most studies, yet uncertainty about its magnitude or direction is rarely quantified. METHODS The authors present a method for probabilistic sensitivity analysis to quantify likely effects of misclassification of a dichotomous outcome, exposure or covariate. This method involves reconstructing the data that would have been observed had the miscla...