BACKGROUND Polyparasitism can lead to severe disability in endemic populations. Yet, the association between soil-transmitted helminth (STH) and the cumulative incidence of Schistosoma japonicum infection has not been described. The aim of this work was to quantify the effect of misclassification error, which occurs when less than 100% accurate tests are used, in STH and S. japonicum infection ...

BACKGROUND AND OBJECTIVE When estimating incidence risk ratios in follow-up studies, subjects testing positive for the disease at baseline are excluded. Although the effect of disease misclassification on estimated incidence risk ratios has otherwise been extensively explored, the effect of disease misclassification at baseline has not previously been analyzed. STUDY DESIGN AND SETTING The de...

BACKGROUND Mortality statistics are used to compare health status of populations; optimally, they base on individual death certificates. However, determining cause of death is error-prone. E.g. cardiovascular disease (CVD) death determination is characterized by sensitivity (SE) and specificity (SP) lower than 85%. Furthermore, differential misclassification may be present in case of homogenous...

Introduction: One of the key sources of performance degradation in the field of time-series classification is the class imbalance problem. In real-world datasets, the minority class (Positive class) is outnumbered by abundant majority (negative) class instances. Objective: Develop a cost-sensitive time-series classification framework. Challenge 1: Minimum classification error criterion (e.g 0-1...

In this article, we discuss how to use a variety of machine learning methods, e.g. tree bagging, random forest, boost, support vector machine, and Gaussian mixture model, for building classifiers for electroencephalogram (EEG) data, which is collected from different brain states on different subjects. Also, we discuss how training data size influences misclassification rate. Moreover, the numbe...

Errors in the collection of data are obstacles to analysis because the underlying misclassification mechanism is usually unknown. Many authors have investigated this problem. In this paper, we shall concentrate on the analysis of contingency tables which are subject to misclassification errors. A general misclassification framework for multi-dimensional contingency tables is proposed. Base on t...