Bayesian clinical classification from high-dimensional data: Signatures versus variability
Bayesian clinical classification from high-dimensional data: Signatures versus variability
复制标题
高维数据的贝叶斯临床分类：特征与变异性
DOI：
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发表时间：
2018
影响因子：
2.3
通讯作者：
A. Coolen
中科院分区：
文献类型：
--
作者：
A. Shalabi;M. Inoue;Johnathan Watkins;E. de Rinaldis;A. Coolen
When data exhibit imbalance between a large number d of covariates and a small number n of samples, clinical outcome prediction is impaired by overfitting and prohibitive computation demands. Here we study two simple Bayesian prediction protocols that can be applied to data of any dimension and any number of outcome classes. Calculating Bayesian integrals and optimal hyperparameters analytically leaves only a small number of numerical integrations, and CPU demands scale as O(nd). We compare their performance on synthetic and genomic data to the mclustDA method of Fraley and Raftery. For small d they perform as well as mclustDA or better. For d = 10,000 or more mclustDA breaks down computationally, while the Bayesian methods remain efficient. This allows us to explore phenomena typical of classification in high-dimensional spaces, such as overfitting and the reduced discriminative effectiveness of signatures compared to intra-class variability.
影响因子：
3.6
作者：
Michael Januszyk;G. Gurtner
通讯作者：
Michael Januszyk;G. Gurtner