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Boosting Cost-Sensitive Trees

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Discovey Science (DS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1532))

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Abstract

This paper explores two techniques for boosting costsensitive trees. The two techniques differ in whether the misclassification cost information is utilized during training. We demonstrate that each of these techniques is good at different aspects of cost-sensitive classifications. We also show that both techniques provide a means to overcome the weaknesses of their base cost-sensitive tree induction algorithm

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, Deakin University, Vic, 3168, Australia

    Kai Ming Ting & Zijian Zheng

Authors
  1. Kai Ming Ting
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  2. Zijian Zheng
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Editor information

Editors and Affiliations

  1. Department of Informatics, Kyushu University, Fukuoka, 812-8581, USA

    Setsuo Arikawa

  2. Institute of Scientific and Industrial Research Devision of Intelligent Systems Science, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan

    Hiroshi Motoda

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© 1998 Springer-Verlag Berlin Heidelberg

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Ting, K.M., Zheng, Z. (1998). Boosting Cost-Sensitive Trees. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_22

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  • DOI: https://doi.org/10.1007/3-540-49292-5_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65390-5

  • Online ISBN: 978-3-540-49292-4

  • eBook Packages: Springer Book Archive

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