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FDR-based categorical variable selection in naïve Bayes classification
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단순 베이즈 분류에서 FDR 기반의 범주형 변수의 선택

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Type
Academic journal
Author
Jieun Shin (서울시립대학교) Changyi Park (서울시립대학교)
Journal
The Korean Data and Information Science Society Journal of the Korean Data And Information Science Society Vol.32 No.6 KCI Excellent Accredited Journal
Published
2021.11
Pages
1,329 - 1,341 (13page)
DOI
10.7465/jkdi.2021.32.6.1329

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FDR-based categorical variable selection in naïve Bayes classification
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Abstract· Keywords

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Naïve Bayes classification is based on the naïve Bayes assumption that explanatory variables are conditionally independent given the response variable. Although the naïve Bayes assumption is rather strong, the naïve Bayes classifier shows reasonable performances and has computational advantages on high-dimensional data. Since high-dimensional data sets usually have many noisy variables, variable selection can improve the accuracy in prediction and the interpretation of the classifier. In this paper, we propose a categorical variable selection method based on FDR control in naïve Bayes classification. Through simulations and real data analysis, the proposed method is compared with another variable selection method based on change point analysis and the proposed methods is illustrated to be more effective, particularly, for sparse or high-dimenional data.
#고차원 데이터 #단순 베이즈 가정 #카이제곱 통계량 #Chi-square statistic #high-dimensional data #naïve Bayes assumption

Contents

요약
1. 서론
2. 분석 방법론
3. 모의실험
4. 실제 데이터 분석
5. 결론
References
Abstract

References (20)

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안치경 / 2018 / 혼합정규분포를 가정한 조건부 상호정보의 준모수적추정량을 이용한 고차원 자료에서의 변수선택 / 한국데이터정보과학회지 29(6):1339~1351 dbpia Askari, A. / 2019 / Na¨ıve featureselection: Sparsity in na¨ıve Bayes / arXiv:1905.098842v2 google schola Benjamini, Y. / 1995 / Controlling the false discovery rate : A practical and powerful approach to multiple testing / Journal of the Royal Statistical Society Series B 57:289~300 google schola Benjamini, Y. / 2001 / The control of the false discovery rate in multiple testing under dependency / Annals of Statistics 29:1165~1188 google schola Choi, B. J. / 2014 / Variable selection for na¨ıve Bayes semisupervised learning / Communications in Statistics-Simulation and Computation 43:2702~2713 google schola

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