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While statistical and artificial intelligence methods such as Artificial NeuralNetworks (ANN) have been used successfully to classify organizations in terms ofsolvency or insolvency, they are limited in degree of generalization either byrequiring linearly separable variables, lack of knowledge of how a conclusion isreached, or lack of a consistent approach for dealing with local optimal solutionwhether maximum or minimum. This research explores the use of a method thathas the ability of the ANN method to deal with linearly inseparable variables andincomplete, noisy data; and resolves the problem of falling into a local optimum insearching the problems space. The paper applies a genetic algorithm to a sample ofU.S. airlines and utilizes financial data from carrier income statements and balancesheets and ratios calculated from this data to assess air carrier solvency.

Subject(s): Research and Development/Tech Change/Emerging Technologies

Research Methods/ Statistical Methods

Issue Date: 2005-03

Publication Type: Conference Paper/ Presentation

PURL Identifier: http://purl.umn.edu/208166

Total Pages: 8

Record appears in: Transportation Research Forum > 46th Annual Transportation Research Forum, Washington, D.C., March 6-8, 2005





Autor: Davalos, Sergio ; Gritta, Richard D. ; Adrangi, Bahram ; Goodfriend, Jason

Fuente: http://ageconsearch.umn.edu/record/208166?ln=en







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