Classification of type I-censored bivariate data

Type I, or limits of detection censoring occurs when a random variable is only observable between fixed and known limits. The classification problem, when the feature vectors to be used to classify are bivariate type I-censored observations, is considered. A Bayes’ optimal classifier is constructed under the assumption that the underlying distribution is Gaussian and it is shown that the decision boundary between classes is not continuous as in the uncensored case. Examples of the decision boundary are presented and simulation studies are used to illustrate the methods described. The resultant classifier is applied to simulated electrical impedance tomography data and a medical data set as illustrations.