Error reduction through learning multiple descriptions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1996-09

AUTHORS

Kamal M. Ali, Michael J. Pazzani

ABSTRACT

Learning multiple descriptions for each class in the data has been shown to reduce generalization error but the amount of error reduction varies greatly from domain to domain. This paper presents a novel empirical analysis that helps to understand this variation. Our hypothesis is that the amount of error reduction is linked to the “degree to which the descriptions for a class make errors in a correlated manner.” We present a precise and novel definition for this notion and use twenty-nine data sets to show that the amount of observed error reduction is negatively correlated with the degree to which the descriptions make errors in a correlated manner. We empirically show that it is possible to learn descriptions that make less correlated errors in domains in which many ties in the search evaluation measure (e.g. information gain) are experienced during learning. The paper also presents results that help to understand when and why multiple descriptions are a help (irrelevant attributes) and when they are not as much help (large amounts of class noise). More... »

PAGES

173-202

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00058611

DOI

http://dx.doi.org/10.1007/bf00058611

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045654538


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