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International Conference on Data Warehousing and Knowledge Discovery

DaWaK 2006: Data Warehousing and Knowledge Discovery pp 218–227Cite as

Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4081))

Abstract

This paper describes problem of prediction that is based on direct marketing data coming from Nationwide Products and Services Questionnaire (NPSQ) prepared by Polish division of Acxiom Corporation. The problem that we analyze is stated as prediction of accessibility to Internet. Unit of the analysis corresponds to a group of individuals in certain age category living in a certain building located in Poland. We used several machine learning methods to build our prediction models. Particularly, we applied ensembles of weak learners and ModLEM algorithm that is based on rough set approach. Comparison of results generated by these methods is included in the paper. We also report some of problems that we encountered during the analysis.

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Jerzy Błaszczyński, Krzysztof Dembczyński & Wojciech Kotłowski

  2. Acxiom Polska, 02-672, Warszawa, Poland

    Mariusz Pawłowski

Authors
  1. Jerzy Błaszczyński
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  2. Krzysztof Dembczyński
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  3. Wojciech Kotłowski
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  4. Mariusz Pawłowski
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Editor information

Editors and Affiliations

  1. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstr. 9-11/188, A-1040, Wien, Austria

    A Min Tjoa

  2. Department of Software and Computing Systems, University of Alicante, Spain

    Juan Trujillo

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Błaszczyński, J., Dembczyński, K., Kotłowski, W., Pawłowski, M. (2006). Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2006. Lecture Notes in Computer Science, vol 4081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823728_21

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  • DOI: https://doi.org/10.1007/11823728_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37736-8

  • Online ISBN: 978-3-540-37737-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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