对自然启发的算法进行比较评估,以解决特征选择问题
Mariappan Premalatha1, Murugan Jayasudha1, Robert Čep2
1School of Computer Science & Engineering, Vellore Institute of Technology, Chennai 600 127, India.
Heliyon
|January 8, 2024
概括
这项研究比较了在机器学习中选择特征的六种自然启发的算法. 人类学习优化和穷和富优化表现出强的性能,提供高精度的强大功能选择.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 特性选择对于减轻大数据中的挑战至关重要,例如无关紧要性,噪音和冗余性.
- 维度的诅咒显著影响机器学习模型的性能和效率.
- 自然启发的算法为复杂的优化问题提供了新的方法,例如特征选择.
研究的目的:
- 实现和比较六种以自然为灵感的算法,用于使用K-最近邻居包装选择特征.
- 根据准确性,特征计数,适应性,收性和计算成本来评估算法性能.
- 在现实世界数据集中确定最有效的算法,以进行强大的特征选择.
主要方法:
- 使用K-近邻 (KNN) 封装方法进行特征选择.
- 采用了六种以自然为灵感的算法:人类学习优化 (HLO),贫富优化 (PRO),灰狼优化器 (GWO),和搜索 (HS) 和另外两种.
- 在六个不同的现实数据集上评估算法性能.
主要成果:
- 人类学习优化 (HLO),贫富优化 (PRO) 和灰狼优化器 (GWO) 在多个性能指标中显示出显著的有效性.
- HLO获得了最高的平均健身,紧随其后的是PRO和和搜索 (HS).
- 在不牺牲分类准确性的情况下,PRO对有效的特征选择特别有希望.
结论:
- 人类启发的算法,特别是PRO,对于强大的特征选择非常有效.
- 这项研究证实了以自然为灵感的技术在解决机器学习中的维度挑战方面的潜力.
- 可以实现优化的特征子集,同时保持高分类性能.
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