Generalised indirect classifiers

• Published in: Computational statistics & data analysis Vol. 49; no. 3; pp. 849 - 861
• Main Authors: Peters, A., Hothorn, T., Lausen, B.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-06-2005; Elsevier Science; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Combining predictive models; Exact sciences and technology; Markov processes; Mathematics; Multivariate analysis; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Statistics; Supervised classification; Supervised classification; Combining predictive models; Learning; Statistical regression; Data analysis; Statistical computation; Predictive distribution; Number field; Simulation model
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2004.06.008

Supervised classifiers are usually based on a set of predictors given in the learning sample as well as in later test samples. Especially in the medical field a reduction of the number of examinations is often desired to save patients time and costs. The approach of indirect classification makes use of all available variables of the learning sample, although it classifies based only on a reduced set of variables. A general definition of indirect classification is given and a specific generalised indirect classifier is proposed. This classifier combines an arbitrary number of regression models which predict those variables that are not acquired for future observations. The performance of the generalised indirect classifier is investigated by using a simulation model which mimics different kinds of decision surfaces and by the application to different data sets. Misclassification results of direct and indirect classifiers are compared.