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Discriminant Analysis of Binary Data by Using the Maximum Entropy Distribution
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 Title & Authors
Discriminant Analysis of Binary Data by Using the Maximum Entropy Distribution
Lee, Jung Jin; Hwang, Joon;
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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
Classification Analysis;Binary Data;Maximum Entropy Distribution;
 Language
Korean
 Cited by
1.
Discriminant Analysis of Binary Data with Multinomial Distribution by Using the Iterative Cross Entropy Minimization Estimation,;

Communications for Statistical Applications and Methods, 2005. vol.12. 1, pp.125-137 crossref(new window)
2.
엔트로피 분포를 이용한 규칙기반 분류분석 연구,이정진;박해기;

Communications for Statistical Applications and Methods, 2010. vol.17. 4, pp.527-540 crossref(new window)
1.
Discriminant analysis of binary data following multivariate Bernoulli distribution, Expert Systems with Applications, 2011, 38, 6, 7795  crossref(new windwow)
2.
Rule-Based Classification Analysis Using Entropy Distribution, Communications for Statistical Applications and Methods, 2010, 17, 4, 527  crossref(new windwow)
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