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Advances in Artificial Intelligence
Volume 2013 (2013), Article ID 176890, 12 pages
http://dx.doi.org/10.1155/2013/176890
Research Article

Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features

Department of Control, Automation and System Analysis, St. Petersburg State Forest Technical University, Institutski per. 5, St. Petersburg 194021, Russia

Received 11 October 2012; Revised 9 February 2013; Accepted 10 March 2013

Academic Editor: Wolfgang Faber

Copyright © 2013 Lev V. Utkin and Yulia A. Zhuk. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A method for solving a classification problem when there is only partial information about some features is proposed. This partial information comprises the mean values of features for every class and the bounds of the features. In order to maximally exploit the available information, a set of probability distributions is constructed such that two distributions are selected from the set which define the minimax and minimin strategies. Random values of features are generated in accordance with the selected distributions by using the Monte Carlo technique. As a result, the classification problem is reduced to the standard model which is solved by means of the support vector machine. Numerical examples illustrate the proposed method.