一种基于量子的过量抽样方法,用于对高度不平衡和重叠的数据进行分类
Bei Yang1, Guilan Tian1, Joseph Luttrell2
1School of Computer and Artificial Intelligence, National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China.
Experimental biology and medicine (Maywood, N.J.)
|January 28, 2024
概括
一种新的基于量子的过量采样方法 (QOSM) 有效地解决了数据不平衡和类重叠的问题. 这种新的方法提高了不平衡数据集的分类准确性,优于现有的方法.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 量子计算应用 量子计算应用
背景情况:
- 数据不平衡和类重叠显著降低了分类性能.
- 现有的方法往往无法同时解决这两个挑战.
研究的目的:
- 引入一种新的基于量子的过量采样方法 (QOSM) 来解决数据不平衡和类重叠的问题.
- 为了提高对具有挑战性的数据集的分类性能.
主要方法:
- QOSM采用量子潜能理论来计算样品的潜在能量.
- 构造性覆盖算法选择最佳覆盖中心,重点关注重叠的区域.
- 过量采样适用于少数类覆盖,以减少不平衡比率 (IR).
主要成果:
- 在SVM,KNN和NB分类器中,QOSM显著提高了分类准确性.
- 该方法与现有的过量采样技术相比,显示出更高的性能.
- 对10个不平衡的KEEL数据集进行了评估,重叠程度各不相同.
结论:
- QOSM有效地减轻了数据不平衡和类重叠.
- 该方法显示了广泛的应用性和与各种分类器的兼容性.
- 在高度不平衡和重叠的数据上,QOSM提供了一个有前途的解决方案,用于改进分类.
相关概念视频
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