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Assessing Post-Radiotherapy Treatment Involving Brain Volume Differences in Children: An Application of Adaptive Systems Methodology Read chapter Read first chapter

2013 | OriginalPaper | Chapter

5. Meta Net: A New Meta-Classifier Family

Authors : Massimo Buscema, William J. Tastle, Stefano Terzi

Published in: Data Mining Applications Using Artificial Adaptive Systems

Publisher: Springer New York

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Abstract

An innovative taxonomy for the classification of classifiers is presented. This new family of meta-classifiers called Meta-Net, having its foundation in the theory of independent judges, is introduced, defined, described, and shown to possess very good performance when compared to other known meta-classifiers.

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Footnotes
1
We gratefully acknowledge Marco Intraligi (Semeion Staff) who helped the authors during the training sessions.
 
2
This detailed analysis of the shared misclassifications among algorithms was conducted thanks to a suggestion of Dr. Giulia Massini (Semeion researcher).
 
3
We acknowledge Dr. Giulia Massini (2010–2011) (Semeion researcher) who has generated this dendrogram, using specific software she wrote.
 
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Metadata
Title
Meta Net: A New Meta-Classifier Family
Authors
Massimo Buscema
William J. Tastle
Stefano Terzi
Copyright Year
2013
Publisher
Springer New York
Book
Data Mining Applications Using Artificial Adaptive Systems

Print ISBN: 978-1-4614-4222-6

Electronic ISBN: 978-1-4614-4223-3

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-4223-3_5

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