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

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.


Subject(s):
Issue Date:
2007
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/206886
Published in:
Journal of the Transportation Research Forum, Volume 46, Number 2
Page range:
62-81
Total Pages:
20




 Record created 2017-04-01, last modified 2017-08-22

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