B
		
		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
		
	
		
			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
			 
		
		
	
		
			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. 
			 
			
			
				
					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.
				  
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