OPTIMAL BAYESIAN FEATURE SELECTION WITH MISSING DATA

被引：0

作者：

Pour, Ali Foroughi ^{[1
]}

Dalton, Lori A. ^{[1
]}

机构：

[1] Ohio State Univ, Columbus, OH 43210 USA

来源：

2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2016年

关键词：

Feature selection; Biomarker discovery; Missing data;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We present a framework for optimal Bayesian feature selection and missing value estimation. Based on this framework, we derive optimal algorithms under an independent Gaussian model, and provide fast sub-optimal methods with superb performance for a dependent Gaussian model.

引用

收藏

页码：35 / 39

页数：5

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