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Using Generalized Estimating Equation to Learn Decision Tree with Multivariate Responses

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Abstract

Previous decision tree algorithms have used Mahalanobis distance for multiple continuous longitudinal response or generalized entropy index for multiple binary responses. However, these methods are limited to either continuous or binary responses. In this paper, we suggest a new tree-based method that can analyze any type of multiple responses by using a statistical approach, called GEE (generalized estimating equations). The value of this new technique is demonstrated with reference to an application using web-usage survey.

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Authors and Affiliations

  1. Institute of Statistics, Korea University, 5-1, Anam-Dong, Sungbuk-ku, Seoul, 136-701, Korea

    Seong Keon Lee

  2. Department of Informational Statistics, Hoseo University, 29-1, Asan, 336-795, Korea

    Hyun-Cheol Kang & Sang-Tae Han

  3. Department of Medical Record, Dankuk University Hospital, Chonan, 330-715, Korea

    Kwang-Hwan Kim

Authors
  1. Seong Keon Lee
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  2. Hyun-Cheol Kang
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  3. Sang-Tae Han
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  4. Kwang-Hwan Kim
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Corresponding author

Correspondence to Seong Keon Lee.

Additional information

This work was supported by grant No. R05-2003-000-11281-0 from the Basic Research Program of the Korea Science & Engineering Foundation.

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Lee, S.K., Kang, HC., Han, ST. et al. Using Generalized Estimating Equation to Learn Decision Tree with Multivariate Responses. Data Min Knowl Disc 11, 273–293 (2005). https://doi.org/10.1007/s10618-005-0004-8

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  • DOI: https://doi.org/10.1007/s10618-005-0004-8

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