Knowledge and Information Systems

, Volume 31, Issue 3, pp 433–454 | Cite as

Improvement of neural network classifier using floating centroids

  • Lin Wang
  • Bo Yang
  • Yuehui Chen
  • Ajith Abraham
  • Hongwei Sun
  • Zhenxiang Chen
  • Haiyang Wang
Regular Paper


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.


Classification Neural networks Floating Centroids Method 


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Lin Wang
    • 1
    • 2
  • Bo Yang
    • 1
  • Yuehui Chen
    • 1
  • Ajith Abraham
    • 3
    • 4
  • Hongwei Sun
    • 5
  • Zhenxiang Chen
    • 1
  • Haiyang Wang
    • 2
  1. 1.Shandong Provincial Key Laboratory of Network based Intelligent ComputingUniversity of JinanJinanChina
  2. 2.School of Computer Science and TechnologyShandong UniversityJinanChina
  3. 3.Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic
  4. 4.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceWashingtonUSA
  5. 5.School of ScienceUniversity of JinanJinanChina

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