链混合特征选择算法基于改进的灰狼优化算法
Xiaotong Bai1, Yuefeng Zheng1, Yang Lu1
1School of Mathematics and Computer, Jilin Normal University, Siping, Jilin, China.
PloS one
|October 8, 2024
概括
一个新的混合特征选择算法,Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization (TMKMCRIGWO),提高了分类准确度和尺寸缩小率. 这种算法可以提高分类准确度和尺寸缩小率. 这种方法在复杂的问题解决中优于单个算法.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 生物信息学是一种生物信息学.
背景情况:
- 单一特征选择方法通常在有效性和性能方面存在局限性.
- 结合不同技术的混合方法可以克服这些限制.
- 需要先进的算法来改善复杂数据集中的特征选择.
研究的目的:
- 为了提出一种新的混合特征选择算法,TMKMCRIGWO.
- 为了提高分类准确性和尺寸缩小率.
- 证明拟议的算法在现有方法上的优越性.
主要方法:
- TMKMCRIGWO算法采用一个两阶段的过方法,使用最大肯德尔最小奇平方 (MKMC) 和ReliefF.
- 一个包装算法,一个改进的灰狼优化 (IGWO) 随机干扰因子,被用于最佳子集选择.
- 过器和包装方法的双重组合旨在实现强大的特征选择.
主要成果:
- 与其他算法相比,TMKMCRIGWO算法在20个数据集中实现了至少0.1%的平均分类准确度增加.
- 平均维度缩小率 (DRR) 达到24.76%,低维数据集的比例更高 (41.04%) 和高维数据集的比例更低 (0.33%).
- 该算法展示了改进的模型概括能力和性能.
结论:
- 与单一方法相比,提议的TMKMCRIGWO算法在特征选择方面提供了更高的性能.
- 混合战略有效地解决了复杂的问题,从而提高了分类准确性和显著的尺寸缩小.
- TMKMCRIGWO显示了提高机器学习模型性能和通用性的前景.
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