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An Approach to Aggregating Ensembles of Lazy Learners that Supports Explanation |
Zenobi, Gabriele; Cunningham, Padraig
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TCD-CS-2002-20 Ensemble research has shown that the aggregated output of an
ensemble of predictors can be more accurate than a single predictor. This is true
also for lazy learning systems like Case-Based Reasoning (CBR) and k-Nearest-
Neighbour. Aggregation is normally achieved by voting in classification tasks
and by averaging in regression tasks. For CBR, this increased accuracy comes
at the cost of interpretability however. If we consider the use of retrieved cases
for explanation to be one of the advantages of CBR then this is lost in an
ensemble. This is because a large number of cases will have been retrieved by
the ensemble members. In this paper we present a new technique for
aggregation that obtains excellent results and identifies a small number of cases
for use in explanation. This new approach might be viewed as a transformation
process whereby cases are transformed from their feature based representation
to a representation based on the predictions of ensemble members. This new
representation produces very accurate predictions and allows a small number of
similar neighbours to be identified.
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Keyword(s):
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Computer Science |
Publication Date:
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2002 |
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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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Higher Education Authority |
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Citation(s):
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Zenobi, Gabriele; Cunningham, Padraig. 'An Approach to Aggregating Ensembles of Lazy Learners that Supports Explanation'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2002-20, 2002, pp12 |
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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 05:31:18 Last Updated:
2015-04-10 05:13:58 |
