Towards Objective Data Selection in Bankruptcy Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Gunnersen:2012:CEC,

• title = "Towards Objective Data Selection in Bankruptcy Prediction",
• author = "Sverre Gunnersen and Kate Smith-Miles and Vincent Lee",
• pages = "9--16",
• booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation",
• year = "2012",
• editor = "Xiaodong Li",
• month = "10-15 " # jun,
• DOI = "doi:10.1109/CEC.2012.6256129",
• address = "Brisbane, Australia",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Conflict of Interest Papers, Computational Intelligence in Finance, Economics and Management Sciences (IEEE-CEC), Large-scale problems.",
• 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.",
• 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