A classification learning algorithm robust to irrelevant features

  • H. Altay Güvenir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1480)

Abstract

Presence of irrelevant features is a fact of life in many real-world applications of classification learning. Although nearest-neighbor classification algorithms have emerged as a promising approach to machine learning tasks with their high predictive accuracy, they are adversely affected by the presence of such irrelevant features. In this paper, we describe a recently proposed classification algorithm called VFI5, which achieves comparable accuracy to nearest-neighbor classifiers while it is robust with respect to irrelevant features. The paper compares both the nearest-neighbor classifier and the VFI5 algorithms in the presence of irrelevant features on both artificially generated and real-world data sets selected from the UCI repository.

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Copyright information

© Springer-Verlag 1998

Authors and Affiliations

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

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