|
Using Early Stopping to Reduce Overfitting in Wrapper-Based Feature Weighting |
Loughrey, John; Cunningham, Padraig
|
|
|
|
TCD-CS-2005-41 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. We demonstrate that the problem of overfitting in feature
weighting can be exacerbated if the feature weighting is fine grained.
With greater representational power we risk learning not only the signal,
but also the idiosyncrasies of the training data. In this paper we show
that both of these effects can be ameliorated by the early-stopping strategy we present. Using this strategy feature weighting will outperform
feature selection in most cases.
|
|
Keyword(s):
|
Computer Science |
Publication Date:
|
2005 |
|
Type:
|
Report |
|
Peer-Reviewed:
|
Unknown |
|
Language(s):
|
English |
|
Institution:
|
Trinity College Dublin |
|
Citation(s):
|
Loughrey, John; Cunningham, Padraig. 'Using Early Stopping to Reduce Overfitting in Wrapper-Based Feature Weighting'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2005-41, 2005, pp12 |
|
Publisher(s):
|
Trinity College Dublin, Department of Computer Science |
|
File Format(s):
|
application/pdf |
|
First Indexed:
2014-05-13 05:31:09 Last Updated:
2015-04-10 05:13:50 |
