一个基于模糊粗略最小分类错误的块矩阵增量特征选择方法
Zhanwei Chen1, Minggang Xing2, Juan Li3
1College of Computer Science and Technology, Xinjiang Normal University, Urumqi, 830054, China.
这项研究引入了一种全新的全球样本内产品相关性,用于模糊粗集模型中的特征选择. 新方法提高了准确性和计算效率,特别是在动态数据场景中.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 内产品相关性是模糊粗集模型中特征选择的关键,估计最小的分类错误.
- 使用样本子集的现有方法限制了捕获全球数据结构的准确性.
研究的目的:
- 提出一个以全球样本为导向的内部产品相关性标准,用于增强特征评估.
- 开发静态和增量特征选择算法,以提高性能和效率.
主要方法:
- 在整个数据宇宙中构建一个连续的模糊成员结构.
- 杆矩阵计算用于静态最小分类基于错误的特征选择 (MCEFS) 算法.
- 实施动态数据环境的区块智能更新机制,从而实现基于区块矩阵的MCEFS (BM-MCEFS).
主要成果:
- 全球样本标准增强了理论上的可靠性和实际一致性.
- 静态MCEFS算法证明了它的有效性和可行性.
- 在基准数据集上,BM-MCEFS显示出卓越的计算效率和数值稳定性.
结论:
- 拟议的全球抽样方法推进了基于产品内部的特征选择.
- 在动态环境中,BM-MCEFS为特征选择提供了高效的解决方案.
- 该研究验证了开发算法的有效性和卓越性能.
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