Study on Feature Selection Based on Fuzzy Clustering Algorithm

  • Quanjin Liu
  • Zhimin Zhao
  • Yong Wang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 124)


Considering the complementarity between the classification and the clustering algorithms, we propose a new feature selection method based on fuzzy Interactive Self-Organizing Data Algorithm (ISODATA). A formula for computing the features’ contribution to class separability in feature space is first defined on the basis of the fuzzy ISODATA. Then, candidate feature subsets are generated according to the feature’s contribution in the procedure of recursive feature elimination process, and the optimal candidate feature subset with the lowest object function, which is the number of misclassified and misclustered samples, is selected from the candidate feature subsets. The proposed method is applied to the acute leukemia gene expression profile dataset. The experiment result shows that the selected features have good performance in terms of both classification and clustering measurements. This demonstrates that our algorithm is effective for selecting informative features from high dimensional dataset.


Feature Selection Feature Subset Feature Selection Method Membership Degree Fuse Classification 
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 2012

Authors and Affiliations

  • Quanjin Liu
    • 1
    • 2
  • Zhimin Zhao
    • 1
  • Yong Wang
    • 3
  1. 1.College of ScienceUniversity of Aeronautics and AstronauticsNanjingChina
  2. 2.Anqing Normal CollegeAnqingChina
  3. 3.The Second Affiliated HospitalAnhui Medical UniversityHefeiChina

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