Advertisement

A New Paradigm for Pattern Classification: Nearest Border Techniques

  • Yifeng Li
  • B. John Oommen
  • Alioune Ngom
  • Luis Rueda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)

Abstract

There are many paradigms for pattern classification. As opposed to these, this paper introduces a paradigm that has not been reported in the literature earlier, which we shall refer to as the Nearest Border (NB) paradigm. The philosophy for developing such a NB strategy is as follows: Given the training data set for each class, we shall first attempt to create borders for each individual class. After that, we advocate that testing is accomplished by assigning the test sample to the class whose border it lies closest to. This claim is actually counter-intuitive, because unlike the centroid or the median, these border samples are often “outliers” and are, really, the points that represent the class the least. However, we have formally proven this claim, and the theoretical results have been verified by rigorous experimental testing.

Keywords

Pattern Classification Border Identification SVM 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Mitchell, T.: Machine Learning. McGraw Hill, Ohio (1997)zbMATHGoogle Scholar
  3. 3.
    Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. PAMI 32(11), 2106–2112 (2010)CrossRefGoogle Scholar
  4. 4.
    Scholkopf, B., Smola, A., Williamson, B., Bartlett, P.: New support vector algorithm. Neural Computation 12, 1207–1245 (2000)CrossRefGoogle Scholar
  5. 5.
    Tax, D., Duin, R.: Support vector domain description. Pattern Recognition Letters 20, 1191–1199 (1999)CrossRefGoogle Scholar
  6. 6.
    Thomas, A., Oommen, B.J.: The fundamental theory of optimal “anti-Bayesian” parametric pattern classification using order statistics criteria. Pattern Recognition 46, 376–388 (2013)CrossRefzbMATHGoogle Scholar
  7. 7.
    Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statistical Science 18(1), 104–117 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Vapnik, V.: Statistical Learning Theory. Wiley-IEEE Press, New York (1998)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Yifeng Li
    • 1
  • B. John Oommen
    • 2
    • 3
  • Alioune Ngom
    • 1
  • Luis Rueda
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorCanada
  2. 2.School of Computer ScienceCarleton UniversityCanada
  3. 3.University of AgderGrimstadNorway

Personalised recommendations