在多视图堆叠中选择视图:选择元学习者
Wouter van Loon1, Marjolein Fokkema1, Botond Szabo2,3,4
1Department of Methodology and Statistics, Leiden University, Leiden, The Netherlands.
概括
多视图堆叠将来自不同来源的数据结合在一起. 非负拉索,自适应拉索和弹性网是选择重要数据视图和提高基因表达研究分类准确度的最佳方法.
科学领域:
- 机器学习 机器学习
- 生物信息学是一种生物信息学.
- 统计建模 统计建模
背景情况:
- 多视图堆叠集成了来自不同特征集 (视图) 的信息,用于对象分析.
- 之前的工作证明了堆叠惩罚后勤回归在识别预测数据视图中的实用性.
- 这项研究通过探索各种元学习者算法来扩展多视图堆叠研究.
研究的目的:
- 在多视图堆叠框架内评估七种不同的元学习算法的视图选择和分类性能.
- 确定最佳的超学习者,用于需要精确的视图选择和高分类性能的应用,特别是在基因表达数据分析中.
主要方法:
- 使用七种不同的元学习算法实现了多视图堆叠框架.
- 进行模拟并分析了两个现实世界的基因表达数据集,以评估性能.
- 基于其执行视图选择和提高分类准确性的能力来评估算法.
主要成果:
- 非阴性拉索,非阴性适应拉索和非阴性弹性网在视图选择和分类准确性方面都表现出卓越的性能.
- 在这三个表现最好的超级学习者中进行选择取决于具体的研究要求.
- 其他评估的元学习者 (非负回归,前进选择,稳定性选择,插入预测器) 提供了有限的优势.
结论:
- 对于优先考虑视图选择和分类准确性的研究,建议在多视图堆叠中使用非负的拉索,自适应的拉索和弹性网.
- 在这三个人中选择最好的meta-learner取决于上下文.
- 该研究为优化生物信息学和相关领域的多视图堆叠提供了有价值的见解.
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