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Projection with Double Nonlinear Integrals for Classification

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Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (ICDM 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5077))

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Abstract

In this study, a new classification model based on projection with Double Nonlinear Integrals is proposed. There exist interactions among predictive attributes towards the decisive attribute. The contribution rate of each combination of predictive attributes, including each singleton, towards the decisive attribute can be re presented by a fuzzy measure. We use Double Nonlinear Integrals with respect to the signed fuzzy measure to project data to 2-Dimension space. Then classify the virtual value in the 2-D space projected by Nonlinear Integrals. In our experiments, we compare our classifier based on projection with Double Nonlinear Integrals with the classical method- Naïve Bayes. The results show that our classification model is better than Naïve Bayes.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR

    JinFeng Wang, KwongSak Leung & KinHong Lee

  2. Department of Mathematics, University of Nebraska at Omaha, Omaha, NE 68182, USA

    ZhenYuan Wang

Authors
  1. JinFeng Wang
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  2. KwongSak Leung
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  3. KinHong Lee
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  4. ZhenYuan Wang
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Petra Perner

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© 2008 Springer-Verlag Berlin Heidelberg

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Wang, J., Leung, K., Lee, K., Wang, Z. (2008). Projection with Double Nonlinear Integrals for Classification. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_11

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  • DOI: https://doi.org/10.1007/978-3-540-70720-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70717-2

  • Online ISBN: 978-3-540-70720-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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