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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/13440

Title: Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets
Author: Cunningham, Pádraig
Loughrey, John
Sponsor: Science Foundation Ireland
Keywords: Computer Science
Issue Date: 28-Jan-2005
Publisher: Trinity College Dublin, Department of Computer Science
Citation: Cunningham, Pádraig; Loughrey, John. 'Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2005-17, 2005, pp11
Series/Report no.: Computer Science Technical Report
TCD-CS-2005-17
Abstract: In Wrapper based feature selection, the more states that are visited during the search phase of the algorithm the greater the likelihood of finding a feature subset that has a high internal accuracy while generalizing poorly. When this occurs, we say that the algorithm has overfitted to the training data. We outline a set of experiments to show this and we introduce a modified genetic algorithm to address this overfitting problem by stopping the search before overfitting occurs. This new algorithm called GAWES (Genetic Algorithm With Early Stopping) reduces the level of overfitting and yields feature subsets that have a better generalization accuracy.
URI: https://www.cs.tcd.ie/publications/tech-reports/reports.05/TCD-CS-2005-17.pdf
http://hdl.handle.net/2262/13440
Appears in Collections:Computer Science Technical Reports

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