Cargando…

Identifying Corporate Performance Factors Based on Feature Selection in Statistical Pattern Recognition Methods, Application, Interpretation.

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-line...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pudil, Pavel
Otros Autores: Blažek, Ladislav, Částek, Ondřej
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Brno : Masarykova univerzita, 2014.
Temas:
Acceso en línea:Texto completo

MARC

LEADER 00000cam a2200000Mu 4500
001 EBOOKCENTRAL_on1226583612
003 OCoLC
005 20240329122006.0
006 m o d
008 201212s2014 xr o ||| 0 eng d
040 |a EBLCP  |b eng  |c EBLCP  |d EBLCP  |d REDDC  |d OCLCO  |d OCLCF  |d HF9  |d OCLCO  |d OCLCQ  |d OCLCO  |d OCLCL 
019 |a 1243118532 
020 |a 9788021076723 
020 |a 8021076720 
029 1 |a AU@  |b 000069466629 
035 |a (OCoLC)1226583612  |z (OCoLC)1243118532 
050 4 |a HD56.25  |b .P835 2014 
082 0 4 |a 658.4013  |2 23 
049 |a UAMI 
100 1 |a Pudil, Pavel. 
245 1 0 |a Identifying Corporate Performance Factors Based on Feature Selection in Statistical Pattern Recognition  |h [electronic resource] :  |b Methods, Application, Interpretation. 
260 |a Brno :  |b Masarykova univerzita,  |c 2014. 
300 |a 1 online resource (0 p.) 
500 |a Description based upon print version of record. 
505 0 |a Intro -- Contents -- Introduction -- 1 Formulation of objectives and methodologicalapproach -- 1.1 Summary of previous research activities -- 1.2 Methodology of current research -- 2 Competitiveness and its measurement -- 2.1 The term competitiveness -- 2.2 Approaches to measuring competitiveness -- 2.3 Financial performance indicators used -- 2.4 Period of performance measurement -- 2.5 The development of performance measurementmethodology -- 2.5.1 Cluster analysis -- 2.5.2 Hyperbola -- 2.5.3 Summation -- 2.5.4 Quintiles 
505 8 |a 2.6 Assessing the appropriateness of methods to measurefinancial performance -- 2.6.1 Experiment settings -- 2.6.2 Experiment output -- 2.7 Description of the methodology used to measureperformance -- 3 Feature Selection Methods in Statistical PatternRecognition -- 3.1 Introduction -- 3.1.1 Common Research Issues in Machine Learning and Management -- 3.2 Dimensionality Reduction -- DR Categorization According to Nature of the Resulting Features -- DR Categorization According to the Aim -- 3.3 Feature Subset Selection -- 3.3.1 FS Categorization With Respect to Optimality 
505 8 |a 3.3.2 FS Categorization With Respect to Selection Criteria -- 3.3.3 FS Categorization With Respect to Problem Knowledge -- 3.4 Sub-optimal Search Methods -- 3.4.1 Best Individual Features -- 3.4.2 Sequential Search Methods and their Evolution -- Floating search methods -- Oscillating search method -- 3.4.3 Non-sequential and alternative methods -- 3.4.4 Pitfalls of feature subset evaluation -- experimental comparisonof criterion functions -- 3.4.5 Summary of recent sub-optimal feature selection methods -- 3.4.6 Dependency-Aware Feature Selection (DAF) -- 3.5 Performance Estimation Problem 
505 8 |a 3.6 Problem of Feature Selection Overfitting and Stability -- 3.6.1 Problem of Feature Selection Stability -- 3.7 Summary -- 4 Testing approaches and methods basedon learning methods for identifying factorsof competitiveness -- 4.1 Introduction -- 4.2 Feature selection based evaluation of competitivenessfactors -- 4.2.1 Feature Selection Methodology -- 4.2.2 Evaluating Stability of Feature Selection Methods -- 4.3 Introducing the modified feature selection methodology -- Non-Parametric Model -- Handling Missing Values and Non-Numeric Values -- 4.4 Pattern classification approach 
505 8 |a 4.5 Regression approach and pseudo-kernel regressionmodel -- 4.6 Experiments and results -- 4.6.1 Regression-based analysis results -- 4.6.2 Classification-based analysis results -- 4.7 Comparing Regression-based and Classification-basedanalysis results -- 4.8 Improved Model for Attribute Selectionon High-Dimensional Economic Data -- 4.8.1 Improvements of the regression model -- 4.8.2 Optimized model performance on 37- and 74-dim data -- 4.9 Conclusions -- 5 Identifying factors of competitiveness usingbivariate analyses and linearregression analyses -- 5.1 General characteristics 
520 |a 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. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Industrial organization  |x Measurement  |v Statistical methods. 
650 0 |a Organizational effectiveness  |x Measurement  |x Statistical methods. 
650 0 |a Industrial productivity  |x Measurement  |x Statistical methods. 
650 6 |a Efficacité organisationnelle  |x Mesure  |x Méthodes statistiques. 
650 6 |a Productivité  |x Mesure  |x Méthodes statistiques. 
650 7 |a Industrial productivity  |x Measurement  |x Statistical methods  |2 fast 
700 1 |a Blažek, Ladislav. 
700 1 |a Částek, Ondřej. 
758 |i has work:  |a Identifying corporate performance factors based on feature selection in statistical pattern recognition (Text)  |1 https://id.oclc.org/worldcat/entity/E39PD3THk6fQFXWrGtDhjfyxtC  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Pudil, Pavel  |t Identifying Corporate Performance Factors Based on Feature Selection in Statistical Pattern Recognition : Methods, Application, Interpretation  |d Brno : Masarykova univerzita,c2014  |z 9788021075573 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6421683  |z Texto completo 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL6421683 
994 |a 92  |b IZTAP