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Avtomat. i Telemekh., 2019, Issue 2, Pages 161–172 (Mi at14983)  

Intellectual Control Systems, Data Analysis

Feature selection using population random search with memory

I. A. Khodashinskii, K. S. Sarin

Tomsk State University of Control Systems and Radioelectronics

Abstract: In this paper, we introduce a new method for solutions to feature selection problems. We develop a technique based on population random search with memory. The concept underlying the proposed method is a combination of random and heuristic search strategies. The solution is represented as a binary vector, the dimension of which is determined by the number of features in the data set. The generation of new solutions is carried out randomly using normal and uniform distribution. The heuristic is formulated as follows: the chance of a sign to get into the next generation is proportional to the frequency of the presence of this feature in previous best solution. The proposed method has been tested on several data sets from the KEEL repository. Comparison with other state-of-the-art feature selection methods demonstrates the competitiveness of the proposed.

Keywords: data mining, feature selection, classification, optimization.

Funding Agency Grant Number
Russian Foundation for Basic Research 16-07-00034_


DOI: https://doi.org/10.1134/S0005231019020107

Full text: PDF file (1091 kB)
First page: PDF file
References: PDF file   HTML file

Document Type: Article
Presented by the member of Editorial Board: V. I. Vasil'ev

Received: 30.01.2018
Revised: 03.06.2018
Accepted: 08.11.2018

Citation: I. A. Khodashinskii, K. S. Sarin, “Feature selection using population random search with memory”, Avtomat. i Telemekh., 2019, no. 2, 161–172

Citation in format AMSBIB
\Bibitem{HodSar19}
\by I.~A.~Khodashinskii, K.~S.~Sarin
\paper Feature selection using population random search with memory
\jour Avtomat. i Telemekh.
\yr 2019
\issue 2
\pages 161--172
\mathnet{http://mi.mathnet.ru/at14983}
\crossref{https://doi.org/10.1134/S0005231019020107}


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