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Improvement of neural network classifier using floating centroids

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Abstract

This paper presents a novel technique—Floating Centroids Method (FCM) designed to improve the performance of a conventional neural network classifier. Partition space is a space that is used to categorize data sample after sample is mapped by neural network. In the partition space, the centroid is a point, which denotes the center of a class. In a conventional neural network classifier, position of centroids and the relationship between centroids and classes are set manually. In addition, number of centroids is fixed with reference to the number of classes. The proposed approach introduces many floating centroids, which are spread throughout the partition space and obtained by using K-Means algorithm. Moreover, different classes labels are attached to these centroids automatically. A sample is predicted as a certain class if the closest centroid of its corresponding mapped point is labeled by this class. Experimental results illustrate that the proposed method has favorable performance especially with respect to the training accuracy, generalization accuracy, and average F-measures.

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, 250022, Jinan, China

    Lin Wang, Bo Yang, Yuehui Chen & Zhenxiang Chen

  2. School of Computer Science and Technology, Shandong University, 250101, Jinan, China

    Lin Wang & Haiyang Wang

  3. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Ajith Abraham

  4. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, 98071, USA

    Ajith Abraham

  5. School of Science, University of Jinan, 250022, Jinan, China

    Hongwei Sun

Authors
  1. Lin Wang
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  2. Bo Yang
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  3. Yuehui Chen
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  4. Ajith Abraham
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  5. Hongwei Sun
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  6. Zhenxiang Chen
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  7. Haiyang Wang
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Correspondence to Bo Yang.

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Wang, L., Yang, B., Chen, Y. et al. Improvement of neural network classifier using floating centroids. Knowl Inf Syst 31, 433–454 (2012). https://doi.org/10.1007/s10115-011-0410-8

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  • DOI: https://doi.org/10.1007/s10115-011-0410-8

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