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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제28권 제1호
- 발행연도
- 2021.1
- 수록면
- 89 - 97 (9page)
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초록· 키워드
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Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.
목차
Abstract
1. Introduction
2. Brief review on Aitchison geometry and ilr transformation
3. Classification on ilr transformed data
4. Numerical studies
5. Conclusion and remarks
References
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