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Using Early-Stopping to Avoid Overfitting in Wrapper-Based Feature Selection Employing Stochastic Search |
Loughrey, John; Cunningham, Padraig
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TCD-CS-2005-37 It is acknowledged that overfitting can occur in feature selection using the wrapper
method when there is a limited amount of
training data available. It has also been
shown that the severity of overfitting is related to the intensity of the search algorithm used during this process. In this paper we show that two stochastic search techniques (Simulated Annealing and Genetic Algorithms) that can be used for wrapper-based
feature selection are susceptible to overfitting in this way. However, because of their
stochastic nature, these algorithms can be
stopped early to prevent overfitting. We
present a framework that implements early-stopping for both of these stochastic search
techniques and we show that this is successful in reducing the effects of overfitting and
in increasing generalisation accuracy in most
cases.
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Keyword(s):
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Computer Science |
Publication Date:
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2005 |
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Type:
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Report |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Trinity College Dublin |
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Citation(s):
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Loughrey, John; Cunningham, Padraig. 'Using Early-Stopping to Avoid Overfitting in Wrapper-Based Feature Selection Employing Stochastic Search'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2005-37, 2005, pp6 |
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Publisher(s):
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Trinity College Dublin, Department of Computer Science |
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File Format(s):
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application/pdf |
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First Indexed:
2014-05-13 05:46:29 Last Updated:
2015-04-10 05:13:51 |
