BGOA-TVG:二元虫优化算法与时间变化的高斯转移函数用于特征选择
Mengjun Li1, Qifang Luo1,2, Yongquan Zhou1,2,3
1College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China.
Biomimetics (Basel, Switzerland)
|March 27, 2024
概括
一个新的二进制草优化算法与时间变化的高斯转移函数 (BGOA-TVG) 增强了特征选择. 这种方法在基准数据集上表现出比传统算法更高的性能.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 群集情报 群集情报 群集情报
背景情况:
- 特性选择对于提高机器学习和数据挖掘中的分类准确性至关重要.
- 传统的二进制优化算法通常使用S形或V形转移函数,这可能会限制合速度和全球搜索能力.
研究的目的:
- 提出一种新的二进制草优化算法,使用时间变化的高斯转移函数 (BGOA-TVG) 进行有效的特征选择.
- 评估BGOA-TVG与传统和最先进的群集智能算法的性能.
主要方法:
- 开发BGOA-TVG算法,结合时间变化的高斯转移函数,将连续搜索空间映射到二进制空间.
- 对BGOA-TVG与S形和V形二元虫优化算法以及其他五个群集智能算法的比较分析.
- 在基准数据集上测试算法:UCI,DEAP和EPILEPSY.
主要成果:
- 与传统传输函数相比,拟议的BGOA-TVG表现出更快的融合速度和更强大的全球搜索能力.
- 在UCI,DEAP和EPILEPSY数据集中,BGOA-TVG在特征选择方面取得了卓越的表现.
- 实验结果表明,BGOA-TVG有效地识别了提高分类准确性的关键特征.
结论:
- BGOA-TVG算法为机器学习中的特征选择提供了一种有效和高效的方法.
- 时间变化的高斯转移函数在优化群集智能算法二进制搜索空间方面提供了显著的优势.
- 通过优化特征选择,BGOA-TVG代表了通过优化特征选择来提高分类准确性的有希望的进步.
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