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Blame-Based Noise Reduction: An Alternative Perspective on Noise Reduction for Lazy Learning |
Pasquier, Francois-Xavier; Delany, Sarah Jane; Cunningham, Padraig
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TCD-CS-2005-29 In this paper we present a new perspective on noise reduction for nearest-neighbour classifiers.
Classic noise reduction algorithms such as Repeated Edited Nearest Neighbour remove cases from
the training set if they are misclassified by their nearest neighbours in a leave-one-out cross validation.
In the approach presented here, cases are identified for deletion based on their propensity
to cause misclassifications. This approach was originally identified in a case-based spam filtering
application where it became clear that certain training examples were damaging to the accuracy of
the system. In this paper we evaluate the general applicability of the approach on a large variety of
datasets and show that it generally beats the classic approach. We also compare the two techniques
on artificial noise and show that both are far from perfect at removing noise and that there remains
scope for further research in this area.
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Keyword(s):
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Nearest-Neighbour Classifiers; Noise Reduction |
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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Funder(s):
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Science Foundation Ireland; Enterprise Ireland |
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Citation(s):
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Pasquier, Francois-Xavier; Delany, Sarah Jane; Cunningham, Padraig. 'Blame-Based Noise Reduction: An Alternative Perspective on Noise Reduction for Lazy Learning'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2005-29, 2005, pp17 |
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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 05:31:07 Last Updated:
2015-04-10 05:13:50 |
