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

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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...

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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:
Industrial organization > Measurement > Statistical methods.
Organizational effectiveness > Measurement > Statistical methods.
Industrial productivity > Measurement > Statistical methods.
Efficacité organisationnelle > Mesure > Méthodes statistiques.
Productivité > Mesure > Méthodes statistiques.
Industrial productivity > Measurement > Statistical methods
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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