提升极端学习机器的峰,全球优化用于分类和回归问题
Carlos Peralez-González1, Javier Pérez-Rodríguez2, Antonio M Durán-Rosal1
1Department of Quantitative Methods, Universidad Loyola Andalucía, Córdoba, Spain.
Scientific reports
|July 21, 2023
概括
这项研究引入了一个新的全球整体极端学习机器 (ELM) 模型,该模型在增强 (BR) 框架内. 这种新方法通过同时优化所有基础学习者来提高回归和分类任务的性能.
科学领域:
- 机器学习 机器学习
- 组合方法 组合方法
- 计算智能是一种计算智能.
背景情况:
- 极端学习机器 (ELM) 使用预配置的隐藏层与输出层优化.
- 现有的Boosting Ridge ELM (BRELM) 实施方案连续训练基础学习者,这可能导致和.
- 传统的合奏方法通常依赖于强大的分类器,这可能并不总是最佳的.
研究的目的:
- 提出一种新的全球学习方法,用于提升 (BR) 框架,使用极端学习机器 (ELM).
- 通过同时优化所有基础学习者来解决BRELM连续培训的局限性.
- 通过允许多种隐藏层配置和避免依赖强大的分类器来提高合奏性能.
主要方法:
- 为BR框架提出了一种全局整体学习方法.
- 基础学习者在单个步骤中同时进行优化,而不是顺序.
- 每个基础学习器都有不同的隐藏层配置.
主要成果:
- 拟议的整体组合方法与原来的BRELM实施相比,显示出更高的性能.
- 统计测试证实了该方法在各种回归和分类基准数据集中的有效性.
- 该方法显示显著改进,无论数据集的特征,如大小,类数或不平衡.
结论:
- 在BR-ELM框架内的新型全球学习方法为回归和分类提供了增强的性能.
- 同时优化各种基础学习者克服了顺序训练和强大的分类器依赖性的局限性.
- 这种方法代表了ELM集体学习的重大进步.
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