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A Meta Learning Approach: Classification by Cluster Analysis

  • Conference paper
Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6304))

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Abstract

This paper describes a new meta-learning technique of combining multiple classifiers based on cluster analysis.

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References

  1. Dzeroski, S., Zenko, B.: Is Combining Classifiers with Stacking Better than Selecting the Best One? Machine Learning, 255–273 (2004)

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  2. Ting, K.M., Witten, I.H.: Stacked generalization: when does it work? In: 15th International Joint Conference on Artificial Intelligence, pp. 866–871 (1997)

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  3. Todorovski, L., Dzeroski, S.: Combining Multiple Models with Meta Decision Trees. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 54–64. Springer, Heidelberg (2000)

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, University of Ulster, Jordanstown campus, Shore Road, Newtownabbey, Co. Antrim, UK, BT37 0QB

    Anna Jurek, Yaxin Bi, Shengli Wu & Chris Nugent

Authors
  1. Anna Jurek
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  2. Yaxin Bi
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  3. Shengli Wu
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  4. Chris Nugent
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Editor information

Editors and Affiliations

  1. Winston-Salem State University, 601 S. Martin Luther King Drive, NC 27110, Winston Salem, USA

    Darina Dicheva

  2. Institute of Information Technologies, Bulgarian Academy of Sciences, 29A Acad. Bonchev Str., 1113, Sofia, Bulgaria

    Danail Dochev

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© 2010 Springer-Verlag Berlin Heidelberg

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Jurek, A., Bi, Y., Wu, S., Nugent, C. (2010). A Meta Learning Approach: Classification by Cluster Analysis. In: Dicheva, D., Dochev, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2010. Lecture Notes in Computer Science(), vol 6304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15431-7_30

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  • DOI: https://doi.org/10.1007/978-3-642-15431-7_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15430-0

  • Online ISBN: 978-3-642-15431-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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