Fast nonparametric classification based on data depth
AbstractA new procedure, called DD-procedure, is developed to solve the problem of classifying d-dimensional objects into q Ï 2 classes. The procedure is completely nonparametric; it uses q-dimensional depth plots and a very efficient algorithm for discrimination analysis in the depth space [0, 1]q . Specifically, the depth is the zonoid depth, and the algorithm is the procedure. In case of more than two classes several binary classifications are performed and a majority rule is applied. Special treatments are discussed for outsiders, that is, data having zero depth vector. The DD-classifier is applied to simulated as well as real data, and the results are compared with those of similar procedures that have been recently proposed. In most cases the new procedure has comparable error rates, but is much faster than other classification approaches, including the SVM. --
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Bibliographic InfoPaper provided by University of Cologne, Department for Economic and Social Statistics in its series Discussion Papers in Statistics and Econometrics with number 1/12.
Date of creation: 2012
Date of revision:
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Alpha-procedure; zonoid depth; DD-plot; pattern recognition; supervised learning; misclassification rate;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-12 (All new papers)
- NEP-CMP-2013-01-12 (Computational Economics)
- NEP-ECM-2013-01-12 (Econometrics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- K. Mosler, 2003. "Central regions and dependency," Econometrics 0309004, EconWPA.
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