Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach
| dc.creator | Davalos, Sergio | |
| dc.creator | Gritta, Richard D. | |
| dc.creator | Adrangi, Bahram | |
| dc.date | 2017-04-01T18:30:40Z | |
| dc.date.accessioned | 2026-07-09T09:25:08Z | |
| dc.description | 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. | |
| dc.identifier | doi:10.22004/ag.econ.206886 | |
| dc.identifier | https://ageconsearch.umn.edu/record/206886/files/1031-1141-1-PB.pdf | |
| dc.identifier | http://ageconsearch.umn.edu/record/206886 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/608726 | |
| dc.language | eng | |
| dc.publisher | ||
| dc.source | http://ageconsearch.umn.edu/record/206886 | |
| dc.title | Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach | |
| dc.type | Text |