贝叶斯多个实例回归中的变量选择使用枪随机搜索
Seongoh Park1,2, Joungyoun Kim3, Xinlei Wang4,5
1School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Korea.
概括
本研究介绍了一种新的贝叶斯回归模型,用于多个实例学习 (MIL),从而提高模型的可解释性. 该方法有效地执行实例和变量选择,提高预测准确性和量化MIL应用的不确定性.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 生物信息学是一种生物信息学.
背景情况:
- 多阶段学习 (MIL) 缺乏可解释的模型.
- 现有的MIL方法往往忽视了模型透明度和不确定性量化.
研究的目的:
- 为MIL开发一个可解释的贝叶斯回归模型.
- 为了同时解决实例和变量选择问题.
- 为MIL预测提供可靠的不确定性量化.
主要方法:
- 提出了一个两级层次的 Bayesian 回归模型.
- 一个修改的猎枪随机搜索算法用于联合离散太空探索.
- 实例和变量选择被整合到模型框架中.
主要成果:
- 该模型在变量和实例选择方面实现了高性能 (AUC > 0.86).
- 它在香数据集上表现出卓越的性能,用于预测分子结合强度.
- 该方法成功地确定了响应建模的相关变量.
结论:
- 拟议的贝叶斯式MIL模型提供了更好的解释性和预测准确性.
- 它有效地识别了关键实例和变量,解决了现代MIL的关键需求.
- 该方法提供了一个严格的框架,用于以MIL计量不确定性量化.
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