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Meta Net: A New Meta-Classifier Family

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Data Mining Applications Using Artificial Adaptive Systems

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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Notes

  1. 1.

    We gratefully acknowledge Marco Intraligi (Semeion Staff) who helped the authors during the training sessions.

  2. 2.

    This detailed analysis of the shared misclassifications among algorithms was conducted thanks to a suggestion of Dr. Giulia Massini (Semeion researcher).

  3. 3.

    We acknowledge Dr. Giulia Massini (2010–2011) (Semeion researcher) who has generated this dendrogram, using specific software she wrote.

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Authors and Affiliations

  1. Semeion Research Center of Sciences of Communication, Via Sersale 117, Rome, Italy

    Massimo Buscema & Stefano Terzi

  2. Department of Mathematical and Statistical Sciences, CCMB, University of Colorado, Denver, Colorado, USA

    Massimo Buscema

  3. Ithaca College, New York, USA

    William J. Tastle

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  1. Massimo Buscema
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  2. William J. Tastle
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  3. Stefano Terzi
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Correspondence to Massimo Buscema .

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Editors and Affiliations

  1. Ithaca College, 424 Park Business School, Ithaca, 14850, USA

    William J. Tastle

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Buscema, M., Tastle, W.J., Terzi, S. (2013). Meta Net: A New Meta-Classifier Family. In: Tastle, W. (eds) Data Mining Applications Using Artificial Adaptive Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4223-3_5

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