Genetic programming and rough sets: A hybrid approach to bankruptcy classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{McKee:2002:EJOR,
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author = "Thomas E. McKee and Terje Lensberg",
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title = "Genetic programming and rough sets: A hybrid approach
to bankruptcy classification",
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journal = "European Journal of Operational Research",
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year = "2002",
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volume = "138",
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pages = "436--451",
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number = "2",
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keywords = "genetic algorithms, genetic programming, Rough sets,
Bankruptcy, Hybrid models, Continuity theory",
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owner = "wlangdon",
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URL = "http://www.sciencedirect.com/science/article/B6VCT-44X69C1-H/2/4757607399cd181dadad865b5a62c58f",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.619.594",
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broken = "http://sedok.narod.ru/s_files/poland/25.pdf",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.619.594",
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DOI = "doi:10.1016/S0377-2217(01)00130-8",
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abstract = "The high social costs associated with bankruptcy have
spurred searches for better theoretical understanding
and prediction capability. we investigate a hybrid
approach to bankruptcy prediction, using a genetic
programming algorithm to construct a bankruptcy
prediction model with variables from a rough sets model
derived in prior research. Both studies used data from
291 US public companies for the period 1991 to 1997.
The second stage genetic programming model developed
consists of a decision model that is 80% accurate on a
validation sample as compared to the original rough
sets model which was 67% accurate. Additionally, the
genetic programming model reveals relationships between
variables that are not apparent in either the rough
sets model or prior research. These findings indicate
that genetic programming coupled with rough sets theory
can be an efficient and effective hybrid modelling
approach both for developing a robust bankruptcy
prediction model and for offering additional
theoretical insights.",
- }
Genetic Programming entries for
Thomas E McKee
Terje Lensberg
Citations