基于杰卡德的多目标进化特征选择,用于高维不平衡数据分类
概括
这项研究引入了基于相似性的Jaccard进化多目标特征选择 (JSEMO) 来解决高维,不平衡的数据. JSEMO增强了多样性,并提高了分类准确性,平衡准确性和g-mean指标.
科学领域:
- 计算智能是一种计算智能.
- 机器学习 机器学习
- 数据挖掘是一种数据挖掘.
背景情况:
- 特性选择 (FS) 方法,过器和封装,在高维,多目标,不平衡的数据集中面临挑战.
- 基于封装的进化FS显示出希望,但需要有效地处理计算成本和性能指标.
研究的目的:
- 提出一种新的基于雅卡德相似性 (JS) 的进化多目标 (JSEMO) 特征选择方法.
- 为了同时解决进化的FS和不平衡的分类器选择.
- 调查特征选择和分类器选择之间的相互影响.
主要方法:
- JSEMO将JS集成到人口初始化,复制和精英主义中,以增强多样性和避免重复解决方案.
- 使用交叉和联合运算符的基于集的变化运算符用于二进制编码兼容性.
- 为不平衡的数据处理引入了一个具有四个目标的双重KNN (KNN2W) 分类器.
主要成果:
- 在15个基准问题中,JSEMO产生了独特的最佳特征,超过了20种现有方法.
- 在整体准确性,平衡准确性和g-mean指标方面观察到显著的改善.
- 保持了可比的特征集大小和计算成本.
结论:
- JSEMO有效地解决了高维,多目标,不平衡的特征选择方面的挑战.
- 整合JS和基于集的变量运算符对算法性能产生了积极的影响.
- 具有适当指标的KNN2W对于处理多目标FS中的不平衡分布至关重要.
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