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GQEO:基于近邻图的通用四边形元素过量抽样对于类不平衡问题.
Qi Dai1, Longhui Wang1, Jing Zhang1
1College of Science, North China University of Science and Technology, Tangshan, 063210, China.
概括
一种新的通用四边元素过量采样技术 (GQEO) 解决了机器学习中的类不平衡问题. 与传统方法不同,GQEO通过考虑全球邻国关系来提高分类器的性能.
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:
- 阶级不平衡对传统分类器的表现构成重大挑战.
- 过量采样技术,如SMOTE,增加了少数群体的实例,但通常依赖于有限的本地信息.
- 现有的方法可能会忽视全球邻居的影响,容易受到噪音的影响.
研究的目的:
- 引入一种新的过量采样技术,即通用四边元素过量采样 (GQEO),旨在克服局部插值方法的局限性.
- 通过整合全球社区信息来增强少数群体实例的特征表示.
- 开发一种可靠的方法,以减轻数据合成期间噪声的影响.
主要方法:
- GQEO使用k-最近邻居 (KNN) 搜索来构建一个全球邻居关系图.
- 它在图中识别了一般化的四边形元素,受平面四边形的约束.
- 少数实例在这些元素中使用受有限元素启发的单维形状函数来合成.
主要成果:
- 30个不平衡数据集的实验结果证明了GQEO在缓解阶级不平衡问题的有效性.
- GQEO成功地防止噪音影响实例合成过程.
- 与最先进的过量采样技术相比,提出的方法实现了竞争性性能,特别是那些解决少数群体噪声的技术.
结论:
- 通过利用全球社区信息,GQEO在解决阶级不平衡问题方面取得了重大进展.
- 该技术为少数实例合成提供了强大的和有效的方法,在各种场景中表现优于现有的方法.
- 在不平衡的数据集上,GQEO代表了提高机器学习分类器性能的有希望的方向.
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