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Author Ricci, F. ♦ Avesani, P.
Sponsorship IEEE Computer Society
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1979
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science ♦ Technology ♦ Medicine & health ♦ Engineering & allied operations
Subject Keyword Data compression ♦ Nearest neighbor searches ♦ Prototypes ♦ Voting ♦ Feedback ♦ Data mining ♦ Independent component analysis ♦ Recurrent neural networks ♦ Machine learning ♦ Neural networks
Abstract A local distance measure for the nearest neighbor classification rule is shown to achieve high compression rates and high accuracy on real data sets. In the approach proposed here, first, a set of prototypes is extracted during training and, then, a feedback learning algorithm is used to optimize the metric. Even if the prototypes are randomly selected, the proposed metric outperforms, both in compression rate and accuracy, common editing procedures like ICA, RNN, and PNN. Finally, when accuracy is the major concern, we show how compression can be traded for accuracy by exploiting voting techniques. That indicates how voting can be successfully integrated with instance-based approaches, overcoming previous negative results.
Description Author affiliation :: Ist. per la Ricerca Sci. e Tecnologica, Povo, Italy
ISSN 01628828
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1999-04-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 21
Issue Number 4
Size (in Bytes) 111.43 kB
Page Count 5
Starting Page 380
Ending Page 384

Source: IEEE Xplore Digital Library