Classification by Voting Feature Intervals

  • Gülşen Demiröz
  • H. Altay Güvenir
Part II: Regular Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1224)


A new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the Naive Bayesian Classifier, which also considers each feature separately. Experiments on real-world datasets show that VFI achieves comparably and even better than NBC in terms of classification accuracy. Moreover, VFI is faster than NBC on all datasets.


Classification Accuracy Feature Dimension Linear Feature Training Instance Nominal Feature 
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 1997

Authors and Affiliations

  • Gülşen Demiröz
    • 1
  • H. Altay Güvenir
    • 1
  1. 1.Department of Computer Engineering and Information ScienceBilkent UniversityAnkaraTurkey

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