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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 1999: Methodologies for Knowledge Discovery and Data Mining pp 504–508Cite as

Prediction Rule Discovery Based on Dynamic Bias Selection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

Abstract

This paper presents an algorithm for discovering prediction rules with dynamic bias selection. A prediction rule, which is aimed at predicting the class of an unseen example, deserves special attention due to its usefulness. However, little attention has been paid to the dynamic selection of biases in prediction rule discovery. A dynamic selection of biases is useful since it reduces humans’ burden of choosing and adjust-ing multiple mining algorithms. In this paper, we propose a novel rule discovery algorithm D 3 BiS, which is based on a data-driven criterion. Our approach has been validated using 17 data sets.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya, Yokohama, 240-8501, Japan

    Toru Ohno

Authors
  1. 1Einoshin Suzuki
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  2. Toru Ohno
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Suzuki, 1., Ohno, T. (1999). Prediction Rule Discovery Based on Dynamic Bias Selection. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_68

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  • DOI: https://doi.org/10.1007/3-540-48912-6_68

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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