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A Supervised Feature Extraction Algorithm for Multi-class

  • Conference paper
Frontiers in Algorithmics (FAW 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5059))

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Abstract

In this paper, a novel supervised information feature extraction algorithm is set up. Firstly, according to the information theories, we carried out analysis for the concept and its properties of the cross entropy, then put forward a kind of lately concept of symmetry cross entropy (SCE), and point out that the SCE is a kind of distance measure, which can be used to measure the difference of two random variables. Secondly, Based on the SCE, the average symmetry cross entropy (ASCE) is set up, and it can be used to measure the difference degree of a multi-class problem. Regarding the ASCE separability criterion of the multi-class for information feature extraction, a novel algorithm for information feature extraction is constructed. At last, the experimental results demonstrate that the algorithm here is valid and reliable, and provides a new research approach for feature extraction, data mining and pattern recognition.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221008, China

    Shifei Ding & Xiaofeng Lei

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China

    Shifei Ding & Zhongzhi Shi

  3. College of Geoinformation Science and Engineering, Shandong University of Science and Technology, Qingdao, 266510, P.R. China

    Fengxiang Jin

Authors
  1. Shifei Ding
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  2. Fengxiang Jin
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  3. Xiaofeng Lei
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  4. Zhongzhi Shi
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Editor information

Franco P. Preparata Xiaodong Wu Jianping Yin

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© 2008 Springer-Verlag Berlin Heidelberg

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Ding, S., Jin, F., Lei, X., Shi, Z. (2008). A Supervised Feature Extraction Algorithm for Multi-class. In: Preparata, F.P., Wu, X., Yin, J. (eds) Frontiers in Algorithmics. FAW 2008. Lecture Notes in Computer Science, vol 5059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69311-6_33

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  • DOI: https://doi.org/10.1007/978-3-540-69311-6_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69310-9

  • Online ISBN: 978-3-540-69311-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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