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Orthogonally Rotational Transformation for Naive Bayes Learning

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Computational Intelligence and Security (CIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3801))

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Abstract

Naive Bayes is one of the most efficient and effective learning algorithms for machine learning, pattern recognition and data mining. But its conditional independence assumption is rarely true in real-world applications. We show that the independence assumption can be approximated by orthogonally rotational transformation of input space. During the transformation process, the continuous attributes are treated in different ways rather than simply applying discretization or assuming them to satisfy some standard probability distribution. Furthermore, the information from unlabeled instances can be naturally utilized to improve parameter estimation without considering the negative effect caused by missing class labels. The empirical results provide evidences to support our explanation.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, JiLin University, ChangChun, 130012, China

    Limin Wang, Haijun Li, Haixia Chen & Liyan Dong

  2. College of Information Science and Engineering, Northeastern University, ShenYang, 110004, China

    Chunhong Cao

  3. College of Computer Science, YanTai University, YanTai, 264005, China

    Haijun Li

Authors
  1. Limin Wang
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  2. Chunhong Cao
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  3. Haijun Li
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  4. Haixia Chen
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  5. Liyan Dong
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Editor information

Editors and Affiliations

  1. Microelectronic Instiute, Xidian University, 710071, Xi’an, China

    Yue Hao

  2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  3. School of Computer Science and Technology, Xidian University, Xi’an, China

    Yuping Wang

  4. Department of Computer Science, Hong Kong Baptist University, Hong Kong,  

    Yiu-ming Cheung

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  6. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  7. Key Laboratory of Computer Networks and Information Security (Ministry of Education), Xidian University, 710071, Xi’an, China

    Jianfeng Ma

  8. National Laboratory of Antennas and Microwave Technology, Xidian University, 710071, Xi’an, Shanxi, P.R. China

    Yong-Chang Jiao

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, L., Cao, C., Li, H., Chen, H., Dong, L. (2005). Orthogonally Rotational Transformation for Naive Bayes Learning. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_21

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  • DOI: https://doi.org/10.1007/11596448_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30818-8

  • Online ISBN: 978-3-540-31599-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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