集群BMA:贝叶斯模型对集群的平均值
Owen Forbes1, Edgar Santos-Fernandez1, Paul Pao-Yen Wu1
1Centre for Data Science, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
PloS one
|August 21, 2023
概括
本研究介绍了clusterBMA,这是一种全新的集合集群方法,通过对多个集群模型的平均结果来提高准确性和量化不确定性. 它的性能优于现有的方法,特别是在复杂的数据集中,为更好的统计通信提供概率集群分配.
科学领域:
- 计算统计和数据挖掘.
- 机器学习和模式识别.
- 生物信息学和计算生物学.
背景情况:
- 传统的集合集群方法经常忽略模型选择的不确定性.
- 贝叶斯模型平均 (BMA) 提供了概率解释和不确定性量化.
- 现有的集合方法可能对模型和参数选择敏感.
研究的目的:
- 引入clusterBMA,一种用于无监督集群的新型加权模型平均方法.
- 为了实现概率集群分配和量化基于模型的不确定性.
- 结合来自不同集群算法的结果,包括硬和软集群.
主要方法:
- 使用集群内部验证标准来近似后置模型概率进行权重.
- 采用对称简单矩阵对组合后方相似矩阵的因数分解,用于最终分配.
- 在附带的R包中实现了clusterBMA方法.
主要成果:
- 集群BMA在模拟数据上优于现有的集群集群方法,特别是在集群分离较低的高维数据上 (1.16-7.12倍改善调整后的Rand指数).
- 在各种维度和集群分离条件下,实现与基准测试方法相当或优于基准测试方法的性能.
- 在一个从电脑电图 (EEG) 数据中识别概率集群的案例研究中证明了实用性.
结论:
- 集群BMA提供了一个强大的方法集群集群,提高准确性和提供有价值的不确定性措施.
- 概率分配和不确定性量化对于临床相关性和应用环境中的统计通信至关重要.
- 该方法有效地整合了多个集群算法的结果,提供了更可靠的集群解决方案.
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