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題名 Nonparametric discriminant analysis with network structures in predictor
作者 陳立榜
Chen, Li-Pang
貢獻者 統計系
關鍵詞 Classification; F-score; graphical models; multivariate kernel estimation; multiclassification; network structure; prediction; supervised learning; surrogate features
日期 2022-06
上傳時間 20-Oct-2022 16:06:34 (UTC+8)
摘要 Multiclassification, known as classification for multi-label responses, has been an important problem in supervised learning and has attracted our attention. Discriminant analysis (DA) is a popular method to deal with multiclassification. With the increasing availability of complex data, it becomes more challenging to analyse them. One of the important features in complex data is the network structure, which is ubiquitous in high-dimensional data because of strong or weak correlations among variables. In addition, in the framework of DA, an assumption of normal distributions is imposed on the predictors, but it is usually invalid in applications. To relax the normality assumption, we propose a nonparametric discriminant function to address multiclassification. In addition, to incorporate the network structure and improve the accuracy of classification, we develop three different network-based surrogate predictors to replace conventional predictors. The key features of the proposed method include the incorporation of network structures in predictors and allowance of predictors to follow exponential family distributions. Finally, numerical studies, including simulation and real data analysis, are conducted to assess the performance of the proposed method.
關聯 Journal of Statistical Computation and Simulation, pp.1-26
資料類型 article
DOI https://doi.org/10.1080/00949655.2022.2084618
dc.contributor 統計系
dc.creator (作者) 陳立榜
dc.creator (作者) Chen, Li-Pang
dc.date (日期) 2022-06
dc.date.accessioned 20-Oct-2022 16:06:34 (UTC+8)-
dc.date.available 20-Oct-2022 16:06:34 (UTC+8)-
dc.date.issued (上傳時間) 20-Oct-2022 16:06:34 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142453-
dc.description.abstract (摘要) Multiclassification, known as classification for multi-label responses, has been an important problem in supervised learning and has attracted our attention. Discriminant analysis (DA) is a popular method to deal with multiclassification. With the increasing availability of complex data, it becomes more challenging to analyse them. One of the important features in complex data is the network structure, which is ubiquitous in high-dimensional data because of strong or weak correlations among variables. In addition, in the framework of DA, an assumption of normal distributions is imposed on the predictors, but it is usually invalid in applications. To relax the normality assumption, we propose a nonparametric discriminant function to address multiclassification. In addition, to incorporate the network structure and improve the accuracy of classification, we develop three different network-based surrogate predictors to replace conventional predictors. The key features of the proposed method include the incorporation of network structures in predictors and allowance of predictors to follow exponential family distributions. Finally, numerical studies, including simulation and real data analysis, are conducted to assess the performance of the proposed method.
dc.format.extent 109 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Journal of Statistical Computation and Simulation, pp.1-26
dc.subject (關鍵詞) Classification; F-score; graphical models; multivariate kernel estimation; multiclassification; network structure; prediction; supervised learning; surrogate features
dc.title (題名) Nonparametric discriminant analysis with network structures in predictor
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1080/00949655.2022.2084618
dc.doi.uri (DOI) https://doi.org/10.1080/00949655.2022.2084618