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Matematicheskaya Biologiya i Bioinformatika, 2015, Volume 10, Issue 2, Pages 356–371
DOI: https://doi.org/10/17537/2015.10.356
(Mi mbb231)
 

Data mining

Computational complexity of prototype and feature selection for isotonic classification problems

A. V. Zukhba

Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region, Russia
References:
Abstract: Decision rules with monotonicity constraints are often used in biomedical diagnostics. Simultaneous feature selection and prototype selection can significantly affect the degree of monotonicity of the data set and, as a consequence, the classification quality. In this paper we propose a systematization of discrete optimization problems of simultaneous feature selection and prototype selection and estimate their computational complexity.
Key words: machine learning, feature selection, prototype selection, isotonic classifier, discrete optimization, computational complexity.
Funding agency Grant number
Russian Foundation for Basic Research 14-07-31240_мол_а
14-07-00847_а
Received 10.09.2015, Published 25.09.2015
Document Type: Article
UDC: 519.7:004.852
Language: Russian
Citation: A. V. Zukhba, “Computational complexity of prototype and feature selection for isotonic classification problems”, Mat. Biolog. Bioinform., 10:2 (2015), 356–371
Citation in format AMSBIB
\Bibitem{Zuk15}
\by A.~V.~Zukhba
\paper Computational complexity of prototype and feature selection for isotonic classification problems
\jour Mat. Biolog. Bioinform.
\yr 2015
\vol 10
\issue 2
\pages 356--371
\mathnet{http://mi.mathnet.ru/mbb231}
\crossref{https://doi.org/10/17537/2015.10.356}
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