Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features
Joint Authors
Source
Advances in Artificial Intelligence
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
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.
American Psychological Association (APA)
Utkin, Lev V.& Zhuk, Yulia A.. 2013. Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features. Advances in Artificial Intelligence،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-452091
Modern Language Association (MLA)
Utkin, Lev V.& Zhuk, Yulia A.. Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features. Advances in Artificial Intelligence No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-452091
American Medical Association (AMA)
Utkin, Lev V.& Zhuk, Yulia A.. Imprecise Imputation as a Tool for Solving Classification Problems with Mean Values of Unobserved Features. Advances in Artificial Intelligence. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-452091
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-452091