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Author Vafaie, H. ♦ Abbott, D. ♦ Hotchins, M. ♦ Matkovsky, I.P.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2000
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Predictive models ♦ Neural networks ♦ Voting ♦ Machine learning algorithms ♦ Machine learning ♦ Training data ♦ Diversity reception ♦ Decision trees ♦ Bagging ♦ Pattern recognition
Abstract Multiple approaches have been developed for improving predictive performance of a system by creating and combining various learned models. There are two main approaches to creating model ensembles. The first is to create a set of learned models by applying an algorithm repeatedly to different training sample data, while the second approach applies various learning algorithms to the same sample data. The predictions of the models are then combined according to a voting scheme. This paper presents a method for combining models that was developed using numerous samples, modeling algorithms, and modelers and compares it with the alternate approaches. The results of the model combination methods are evaluated with respect to sensitivity and false alarm rates and are then compared against other approaches.
Description Author affiliation: Fed.. Data Corp., Bethesda, MD, USA (Vafaie, H.)
ISBN 0769509096
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2000-11-15
Publisher Place Canada
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 877.43 kB
Page Count 8
Starting Page 344
Ending Page 351

Source: IEEE Xplore Digital Library