Meta-learning for data summarization based on instance selection method

Kate Smith-Miles, Rafiqul Islam

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

11 Citations (Scopus)


The purpose 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, as well as the machine learning method. This paper adopts a meta-learning approach, via an empirical study of 112 classification datasets from the UCI Repository, to explore the relationship between data characteristics, machine learning methods, and the success of instance selection method.
Original languageEnglish
Title of host publicationProceedings of the 2010 IEEE Congress on Evolutionary Computation (CEC),
EditorsGary Fogel
Place of PublicationAtlanta USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1 - 8
Number of pages8
Publication statusPublished - 2010
EventIEEE Congress on Evolutionary Computation 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010 (Proceedings)


ConferenceIEEE Congress on Evolutionary Computation 2010
Abbreviated titleIEEE CEC 2010
Internet address

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