Meta-learning of instance selection for data summarization

Kate A. Smith-Miles, MD Rafiqul Islam

Research output: Book chapter/Published conference paperChapter (peer-reviewed)peer-review

2 Citations (Scopus)

Abstract

The goal of instance selection is to identify which instances
(examples, patterns) in a large dataset should be selected as representatives of
the entire dataset, without significant loss of information. When a machine
learning method is applied to the reduced dataset, the accuracy of the model
should not be significantly worse than if the same method were applied to the
entire dataset. The reducibility of any dataset, and hence the success of instance
selection methods, surely depends on the characteristics of the dataset. However
the relationship between data characteristics and the reducibility achieved by
instance selection methods has not been extensively tested. This chapter adopts
a meta-learning approach, via an empirical study of 112 classification datasets,
to explore the relationship between data characteristics and the success of a
nai've instance selection method. The approach can be readily extended to
explore how the data characteristics influence the performance of many more
sophisticated instance selection methods.
Original languageEnglish
Title of host publicationMeta-learning in computational intelligence
EditorsJankowski Norbert, Włodzisław Duch, Gra̧bczewski Krzysztof
Place of PublicationBerlin
PublisherSpringer-Verlag London Ltd.
Chapter2
Pages77-95
Number of pages19
ISBN (Electronic)9783642209802
ISBN (Print)9783642209796
Publication statusPublished - 2011

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