优化高维数据分类与混合AI驱动的功能选择框架和机器学习方案的优化.
Amina Salhi1, Rayan Alshamrani2, Ashrf Althbiti2
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific reports
|October 8, 2025
概括
特性选择 (FS) 通过减少模型复杂性和培训时间,显著提高了分类准确性. TMGWO混合算法在识别关键特征和改善分类结果方面表现出卓越的性能.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 特性选择 (FS) 对高维数据集至关重要,以提高分类准确性.
- FS最大限度地降低了模型的复杂性,减少了训练时间,并提高了概括性.
- 维度的诅咒需要有效的特征选择策略.
研究的目的:
- 评估和比较各种特征选择分类算法.
- 引入和评估用于增强特征识别的新型混合算法.
- 证明特征选择对分类性能指标的影响.
主要方法:
- 在威斯康星州的乳腺癌诊断,声纳和差异化甲状腺癌数据集上进行了实验.
- 评估的标准分类器:K-近邻 (KNN),随机森林 (RF),多层感知器 (MLP),物流回归 (LR) 和支持向量机器 (SVM).
- 引入并测试了混合算法:TMGWO (双相突变灰狼优化),ISSA (改进的Salp Swarm算法) 和BBPSO (二进制黑粒子群优化).
主要成果:
- 在特征选择和分类准确性方面,TMGWO混合方法取得了卓越的结果.
- 对比分析显示,使用FS显著改善了准确性,精度和回忆力.
- TMGWO在识别分类的重要特征方面表现优于其他实验方法.
结论:
- 混合特征选择算法,特别是TMGWO,为分类任务提供了显著的优势.
- 有效的特征选择对于提高模型性能和避免维度的诅咒至关重要.
- 该研究强调了先进的FS技术在机器学习应用中的重要性.
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