Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Discriminant Analysis of Binary Data by Using the Maximum Entropy Distribution

  • Lee, Jung Jin (Department of Statistics, SoongSil University) ;
  • Hwang, Joon (Department of Statistics, Soong Sil University)
  • Published : 2003.12.01
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Abstract

Although many classification models have been used to classify binary data, none of the classification models dominates all varying circumstances depending on the number of variables and the size of data(Asparoukhov and Krzanowski (2001)). This paper proposes a classification model which uses information on marginal distributions of sub-variables and its maximum entropy distribution. Classification experiments by using simulation are discussed.

Keywords

References

  1. Computational Statistics and Data Analysis v.38 A comparison of discriminant procedures for binary variables Asparoukhov,O.K.;Krzanowski,W.J. https://doi.org/10.1016/S0167-9473(01)00032-9
  2. Pattern Classification Johnson,R.;Wichern,D.
  3. Applied Multivariate Statistical Analysis Johnson,R.;Wichern,D.
  4. Discriminant Analysis Lachenbruch

Cited by

  1. Rule-Based Classification Analysis Using Entropy Distribution vol.17, pp.4, 2010, https://doi.org/10.5351/CKSS.2010.17.4.527
  2. Discriminant analysis of binary data following multivariate Bernoulli distribution vol.38, pp.6, 2011, https://doi.org/10.1016/j.eswa.2010.12.126