图形 嵌入式 直觉 模糊 随机 矢量 功能链接 阶级不平衡学习神经网络
IEEE transactions on neural networks and learning systems
|February 9, 2024
概括
本研究介绍了一种新的嵌入图形的直观模糊随机向量功能链接网络 (GE-IFRVFL-CIL),用于解决机器学习中的类不平衡 (CI). 该模型有效地处理不平衡的数据,噪声,并保留数据集结构,以提高分类准确性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 阶级不平衡 (CI) 在机器学习中带来了重大挑战,导致偏见的模型不足以代表少数阶级.
- 随机向量功能链路 (RVFL) 网络虽然有效,但却在与不平衡的数据集作斗争.
研究的目的:
- 提出一个新的模型,GE-IFRVFL-CIL,以克服RVFL网络在CI学习中的局限性.
- 通过结合图形嵌入和直观模糊理论来提高不平衡数据集的分类准确性.
主要方法:
- 开发了一个嵌入图形的直觉模糊RVFL用于CI学习 (GE-IFRVFL-CIL) 模型.
- 集成了一个权重机制来管理不平衡的数据.
- 使用图形嵌入 (GE) 来保存拓数据结构.
- 采用直觉模糊 (IF) 理论来处理数据的不确定性和不精确性.
主要成果:
- 该GE-IFRVFL-CIL模型在KEEL基准不平衡数据集上表现出卓越的性能,即使有高斯噪声.
- 在阿尔茨海默病神经成像计划 (ADNI) 数据集上取得了有希望的结果,展示了现实世界的应用性.
- 该模型有效地减轻了噪音和异常值,同时保留了固有的数据集几何结构.
结论:
- 拟议的GE-IFRVFL-CIL模型为阶级失衡学习提供了一个强大的解决方案.
- 它有效地处理杂和不精确的数据,增强模型的概括性.
- 该方法成功地保留了关键数据结构,从而改善了分类结果.
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