The Practical Method of Fractal Dimensionality Reduction Based on Z-Ordering Technique

  • Guanghui Yan
  • Zhanhuai Li
  • Liu Yuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Feature selection, the process of selecting a feature subset from the original feature set, plays an important role in a wide variety of contexts such as data mining, machine learning, and pattern recognition. Recently, fractal dimension has been exploited to reduce the dimensionality of the data space. FDR(Fractal Dimensionality Reduction) is one of the most famous fractal dimension based feature selection algorithm proposed by Traina in 2000. However, it is inefficient in the high dimensional data space for multiple scanning the dataset. Take advantage of the Z-ordering technique, this paper proposed an optimized FDR, ZBFDR(Z-ordering Based FDR), which can select the feature subset through scanning the dataset once except for preprocessing. The experimental results show that ZBFDR algorithm achieves better performance.


Fractal Dimension Feature Selection Feature Subset Feature Selection Method Feature Selection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guanghui Yan
    • 1
    • 2
  • Zhanhuai Li
    • 1
  • Liu Yuan
    • 1
  1. 1.Dept. Computer Science & Software NorthWestern Polytechnical UniversityXianP.R. China
  2. 2.Key Laboratory of Opto-Electronic Technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of EducationLanzhouP.R. China

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