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PUDIL, Pavel, Ladislav BLAŽEK, Ondřej ČÁSTEK, Petr SOMOL, Jana POKORNÁ and Maria KRÁLOVÁ. Identifying Corporate Performance Factors Based on Feature Selection in Statistical Pattern Recognition: METHODS, APPLICATION, INTERPRETATION. 1. vyd. Brno: Masarykova univerzita, 2014. 170 pp. ISBN 978-80-210-7557-3.
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Basic information
Original name Identifying Corporate Performance Factors Based on Feature Selection in Statistical Pattern Recognition: METHODS, APPLICATION, INTERPRETATION
Authors PUDIL, Pavel (203 Czech Republic), Ladislav BLAŽEK (203 Czech Republic, guarantor, belonging to the institution), Ondřej ČÁSTEK (203 Czech Republic, belonging to the institution), Petr SOMOL (203 Czech Republic), Jana POKORNÁ (203 Czech Republic, belonging to the institution) and Maria KRÁLOVÁ (203 Czech Republic, belonging to the institution).
Edition 1. vyd. Brno, 170 pp. 2014.
Publisher Masarykova univerzita
Other information
Original language English
Type of outcome book on a specialized topic
Field of Study 5.6 Political science
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
RIV identification code RIV/00216224:14560/14:00074365
Organization Ekonomicko-správní fakulta - Repository
ISBN 978-80-210-7557-3
Keywords in English Dependency-Aware Feature Ranking; Feature Selection; Pattern Recognition; Corporate Financial Performance; Competitiveness; Factors; Linear Regression; Non-linear Regression; Sequential Forward Flow Search; k Nearest Neighbours
Links GAP403/12/1557, research and development project.
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 6/11/2015 00:50.
Abstract
This publication summarizes and extends methodology of feature selection (FS) and pattern recognition in search for competitiveness factors and methodology of corporate financial performance (CFP) measurement. Several methods were evaluated and Dependency-Aware Feature Ranking combined with non-linear regression model were applied. Also, this publication suggests and verifies methodology of interpretation results of the FS methods. For start was employed multidimensional linear regression, succeeded by clustering companies according to the factors identified by FS into homogenous groups, dividing them into quartiles based on their CFP and identifying similar values of the factors. This way was captured the non-linearity in the data.
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