Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

Sergio Davalos, Richard D. Gritta and Bahram Adrangi

Journal of the Transportation Research Forum, 2007, vol. 46, issue 2

Abstract: Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model’s performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

Keywords: Public; Economics (search for similar items in EconPapers)
Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/206886/files/1031-1141-1-PB.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:ndjtrf:206886

DOI: 10.22004/ag.econ.206886

Access Statistics for this article

More articles in Journal of the Transportation Research Forum from Transportation Research Forum
Bibliographic data for series maintained by AgEcon Search ().