Towards Objective Data Selection in Bankruptcy Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Gunnersen:2012:CEC,
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title = "Towards Objective Data Selection in Bankruptcy
Prediction",
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author = "Sverre Gunnersen and Kate Smith-Miles and
Vincent Lee",
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pages = "9--16",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6256129",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Conflict of
Interest Papers, Computational Intelligence in Finance,
Economics and Management Sciences (IEEE-CEC),
Large-scale problems.",
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abstract = "This paper proposes and tests a methodology for
selecting features and test cases with the goal of
improving medium term bankruptcy prediction accuracy in
large uncontrolled datasets of financial records. We
propose a Genetic Programming and Neural Network based
objective feature selection methodology to identify key
inputs, and then use those inputs to combine
multi-level Self-Organising Maps with Spectral
Clustering to build clusters. Performing objective
feature selection within each of those clusters, this
research was able to increase out-of-sample
classification accuracy from 71.3percent and
69.8percent on the Genetic Programming and Neural
Network models respectively to 80.0percent and
77.3percent.",
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notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",
- }
Genetic Programming entries for
Sverre Gunnersen
Kate Smith-Miles
Vincent Lee
Citations