Theoretical Analyses of Cross-Validation Error and Voting in Instance-Based Learning

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TypeArticle
Journal titleJournal of Experimental and Theoretical Artificial Intelligence (JETAI)
Volume6
Subjectcross validation; curve fitting; AIC; bias; variance
AbstractThis paper begins with a general theory of error in cross-validation testing ofalgorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values are symbolic. Cross-validation requires a set of training examples and a set of testing examples. The value of the attribute that is to be predicted is known to the learner in the training set, but unknown in the testing set. The theory demonstrates that cross-validation error has two components: error on the training set (inaccuracy) and sensitivity to noise (instability). This general theory is then applied to voting in instance-based learning. Given an example in the testing set, a typical instance-based learning algorithm predicts the designated attribute by voting among the k nearest neighbors (the k most similar examples) to the testing example in the training set. Voting is intended to increase the stability (resistance to noise) of instance-based learning, but a theoretical analysis shows that there are circumstances in which voting can be destabilizing. The theory suggests ways to minimize cross-validation error, by insuring that voting is stable and does not adversely affect accuracy.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number35073
NPARC number8914390
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Record identifier82cd15e5-9759-403c-b307-f0ea24165970
Record created2009-04-22
Record modified2016-05-09
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