PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 15(XV) | nr 2 | 94--101
Tytuł artykułu

Families of Classifiers - Application in Data Envelopment Analysis

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Economic description of firms and companies is based on a number of indicators. The indicators are related to each other and can be considered only in a specific context. Regression models allow for such approach. Unfortunately, the problems we deal with are usually nonlinear and the choice of relevant information is very difficult. The aim of the paper is to present a method of variable selection based on random forest and gradient boosting approach and its application to companies ranking in DEA method. The results will be compared with the ordering obtained using expert supported approach for variable selection in DEA. (original abstract)
Twórcy
  • Warsaw University of Life Sciences - SGGW, Poland
  • Warsaw University of Life Sciences - SGGW, Poland
Bibliografia
  • Andersen P., Petersen N. C. (1993) A Procedure for Ranking Efficient Units in Data Envelopment Analysis, Management Science, Vol. 39, pp.1261-1264.
  • Berk R. A. (2008) Statistical learning from a regression perspective, Springer, New York.
  • Breiman L. (2001) Random Forests, Machine Learning, Vol. 45 (1), pp. 5-32.
  • Chodakowska E., Wardzińska K. (2013) The attempt to create an internal credit risk rating of production companies with the use of Operational Research method, Quantitative Methods in Economics, Vol. XIV, No. 1, pp. 74-83.
  • Cooper W.W., Seiford L.M., Tone K. (2006) Introduction to Data Envelopment Analysis and Its Uses with DEA-Solver Software and References, Springer, New York.
  • Demirova M. (2010) An empirical application of data envelopment analysis in credit rating. Theses and dissertations, Paper 981, Ryerson University, Canada.
  • Dzidzevičiūtė L. (2012) Estimation of default probability for low default portfolios, Ekonomika 2012, Vol. 91 (1), pp.132-156.
  • Feruś A. (2006) The Application of the DEA Method to Define the Level of Company Credit Risk, Bank i Kredyt, Vol. 37, No. 7, pp. 44-59.
  • Hastie T., Tibshirani R., Friedman J. (2009) The elements of statistical learning. Data Mining, Inference and Prediction, Second Edition, Springer, New York.
  • Kaczmarska B. (2010) The Data Envelopment Analysis Method in Benchmarking of Technological Incubators, Operations Research and Decisions, Vol. 20, No. 1, pp. 79-95.
  • Koronacki J., Ćwik J. (2008) Statystyczne systemy uczące się, Akademicka Oficyna Wydawnicza EXIT, Warszawa.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171326369

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.