Maximizing Benefit of Classifications Using Feature Intervals

  • Nazlı İkizler
  • H. Altay Güvenir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2773)


There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means of a new classification algorithm, Benefit-Maximizing classifier with Feature Intervals (BMFI) that uses feature projection based knowledge representation. Empirical results show that BMFI has promising performance compared to recent cost-sensitive algorithms in terms of the benefit gained.


Training Instance Minority Class Feature Projection Vote Method Consecutive Interval 
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 2003

Authors and Affiliations

  • Nazlı İkizler
    • 1
  • H. Altay Güvenir
    • 1
  1. 1.Department of Computer EngineeringBilkent UniversityAnkaraTurkey

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