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Diversity-Driven Widening

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Advances in Intelligent Data Analysis XII (IDA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8207))

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Abstract

This paper follows our earlier publication [1], where we introduced the idea of tuned data mining which draws on parallel resources to improve model accuracy rather than the usual focus on speed-up. In this paper we present a more in-depth analysis of the concept of Widened Data Mining, which aims at reducing the impact of greedy heuristics by exploring more than just one suitable solution at each step. In particular we focus on how diversity considerations can substantially improve results. We again use the greedy algorithm for the set cover problem to demonstrate these effects in practice.

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Authors and Affiliations

  1. Dept. of CIS and Graduate School Chemical Biology (KoRS-CB), University of Konstanz, 78457, Konstanz, Germany

    Violeta N. Ivanova & Michael R. Berthold

Authors
  1. Violeta N. Ivanova
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  2. Michael R. Berthold
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Editor information

Editors and Affiliations

  1. School of Information Systems, Computing and Mathematics, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK

    Allan Tucker  & Stephen Swift  & 

  2. Faculty of Computer Science/IT, Ostfalia University of Applied Sciences, Am Exer 2, 38302, Wolfenbüttel, Germany

    Frank Höppner

  3. Faculty of Science, Department of Information and Computing Science, Buys Ballot Laboratory, Universiteit Utrecht, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Arno Siebes

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Ivanova, V.N., Berthold, M.R. (2013). Diversity-Driven Widening. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_20

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  • DOI: https://doi.org/10.1007/978-3-642-41398-8_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41397-1

  • Online ISBN: 978-3-642-41398-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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