Přehled o publikaci
2014
Identifying Corporate Performance Factors Based on Feature Selection in Statistical Pattern Recognition: METHODS, APPLICATION, INTERPRETATION
PUDIL, Pavel; Ladislav BLAŽEK; Ondřej ČÁSTEK; Petr SOMOL; Jana POKORNÁ et. al.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
Language
English
Type of outcome
Book on a specialized topic
Field of Study
Management, administration and clerical work
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 – 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: 2/9/2020 00:18, RNDr. Daniel Jakubík
Abstract
In the original language
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.
