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Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error |
Zenobi, Gabriele; Cunningham, Padraig
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TCD-CS-2001-11 It is well known that ensembles of predictors produce better accuracy
than a single predictor provided there is diversity in the ensemble. This
diversity manifests itself as disagreement or ambiguity among the ensemble
members. In this paper we focus on ensembles of classifiers based on different
feature subsets and we present a process for producing such ensembles that
emphasizes diversity (ambiguity) in the ensemble members. This emphasis on
diversity produces ensembles with low generalization errors from ensemble
members with comparatively high generalization error. We compare this with
ensembles produced focusing only on the error of the ensemble members
(without regard to overall diversity) and find that the ensembles based on
ambiguity have lower generalization error. Further, we find that the ensemble
members produced focusing on ambiguity have less features on average that
those based on error only. We suggest that this indicates that these ensemble
members are local learners.
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Keyword(s):
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Computer Science |
Publication Date:
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2001 |
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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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Zenobi, Gabriele; Cunningham, Padraig. 'Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2001-11, 2001, pp15 |
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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 05:42:08 Last Updated:
2015-04-10 05:14:03 |
