政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142453

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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/142453

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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:	2022-10-20 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 連結:	https://doi.org/10.1080/00949655.2022.2084618
DOI:	10.1080/00949655.2022.2084618
顯示於類別:	[統計學系] 期刊論文

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