Discriminant Analysis of Binary Data with Multinomial Distribution by Using the Iterative Cross Entropy Minimization Estimation

- Journal title : Communications for Statistical Applications and Methods
- Volume 12, Issue 1, 2005, pp.125-137
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CKSS.2005.12.1.125

Title & Authors

Discriminant Analysis of Binary Data with Multinomial Distribution by Using the Iterative Cross Entropy Minimization Estimation

Lee Jung Jin;

Lee Jung Jin;

Abstract

Many discriminant analysis models for binary data have been used in real applications, but none of the classification models dominates in all varying circumstances(Asparoukhov & Krzanowski(2001)). Lee and Hwang (2003) proposed a new classification model by using multinomial distribution with the maximum entropy estimation method. The model showed some promising results in case of small number of variables, but its performance was not satisfactory for large number of variables. This paper explores to use the iterative cross entropy minimization estimation method in replace of the maximum entropy estimation. Simulation experiments show that this method can compete with other well known existing classification models.

Keywords

Discriminant Analysis;Binary Data;Multinomial Distribution;Cross Entropy;

Language

English

Cited by

1.

엔트로피 분포를 이용한 규칙기반 분류분석 연구,이정진;박해기;

2.

Cluster Analysis with Balancing Weight on Mixed-type Data,Chae, Seong-San;Kim, Jong-Min;Yang, Wan-Youn;

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