Bias-Corrected Hierarchical Bayesian Classification With a Selected Subset of High-Dimensional Features
基于高维特征筛选子集的偏差校正层级贝叶斯分类AI 解读 ↓
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基于高维特征筛选子集的偏差校正层级贝叶斯分类
- 研究问题
- 按特征与响应的关联筛选高维变量后，如何校正筛选造成的信噪比夸大，避免未来样本的预测概率过度自信？
- 方法与思路
- 作者提出 BCBCSF，将保留特征与筛选过程的部分信息共同纳入层级贝叶斯模型，用 MCMC 推断参数并预测新样本。
- 材料陈述的贡献
- 摘要称该方法能够校正相关超参数后验的筛选偏差，并在模拟及两个微阵列数据集上获得优于两种对照方法的预测。
- 阅读关注点
- 应核对筛选信息如何进入后验、校正适用的筛选规则，以及预测比较使用的损失函数、概率校准指标和验证设计。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；摘要未说明校正成立的具体条件、数值改善幅度或比较结果的外推范围。
原文摘要
Class prediction based on high-dimensional features has received a great deal of attention in many areas of application. For example, biologists are interested in using microarray gene expression profiles for diagnosis or prognosis of a certain disease (e.g., cancer). For computational and other reasons, it is necessary to select a subset of features before fitting a statistical model, by evaluating how strongly the features are related to the response. However, such a feature selection procedure will result in overconfident predictive probabilities for future cases, because the signal-to-noise ratio in the retained features is exacerbated by the feature selection. In this article we develop a hierarchical Bayesian classification method that can correct for this feature selection bias. Our method, which we term bias-corrected Bayesian classification with selected features (BCBCSF), uses the partial information from the feature selection procedure, in addition to the retained features, to form a correct (unbiased) posterior distribution of certain hyperparameters in the hierarchical Bayesian model that control the signal-to-noise ratio of the dataset. We take a Markov chain Monte Carlo (MCMC) approach to inferring the model parameters. We then use MCMC samples to make predictions for future cases. Because of the simplicity of the models, the inferred parameters from MCMC are easy to interpret, and the computation is very fast. Simulation studies and tests with two real microarray datasets related to complex human diseases show that our BCBCSF method provides better predictions than two widely used high-dimensional classification methods, prediction analysis for microarrays and diagonal linear discriminant analysis. The R package BCBCSF for the method described here is available from http://math.usask.ca/longhai/software/BCBCSF and CRAN.