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  • 标题:CLASSIFICATION BINARY MODELS FOR BIOMEDICAL DATA: SIMPLE PROBABILISTIC NETWORKS AND LOGISTIC REGRESSION
  • 本地全文:下载
  • 作者:Anderson ARA ; Francisco LOUZADA ; Luis Aparecido MILAN
  • 期刊名称:Revista Brasileira de Biometria
  • 印刷版ISSN:0102-0811
  • 电子版ISSN:1983-0823
  • 出版年度:2018
  • 卷号:36
  • 期号:1
  • 页码:48-55
  • DOI:10.28951/rbb.v36i1.114
  • 出版社:Universidade Federal de Lavras
  • 摘要:In the biomedical area a critical factor is whether a classication model is accurate enough in order to provide correct classication whether or not a patient has a certain disease. Several techniques may be used in order to accommodate such situation.In this context, Bayesian networks have emerged as a practical classication technology with successful applications in many elds. At the same time, logistic regression is a widely used statistical classication method and evidenced in the literature. In the current paper we focus on investigating the preditive performance of a probabilistic networks in its simple particular case, the so called naive Bayes network, compared to the logistic regression. A systematic simulation study is performed and the procedures are illustrated in some benchmark biomedical data sets.data sets.
  • 关键词:Binary classification; simple probabilistic networks; na¨ıve Bayes; logistic regression.
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