Trinity College Dublin, Department of Computer Science
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
Computer Science Technical Report TCD-CS-2005-17
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.
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