优化多式联络功能选择使用二进制增强子搜索算法,以提高分类性能
Kalaipriyan Thirugnanasambandam1, Jayalakshmi Murugan2, Rajakumar Ramalingam3
1Centre for Smart Grid Technologies, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
PeerJ. Computer science
|March 4, 2024
概括
本研究介绍了二元增强子搜索算法 (BRCSA) 用于多模式特征选择,显著提高了分类准确性. 在数据挖掘和机器学习应用中,BRCSA方法在现有方法中表现优越.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 计算智能是一种计算智能.
背景情况:
- 在数据挖掘中,特征选择对于准确的分类和知识表示至关重要.
- 选择一个最佳的功能子集是具有挑战性的,影响计算成本和准确性.
- 多模式数据为有效的特征选择带来了独特的挑战.
研究的目的:
- 为多式联运特征选择引入一种新的优化算法.
- 通过从多个数据模式中识别最相关的特征来提高分类性能.
- 解决特征选择中的计算效率和准确性挑战.
主要方法:
- 开发了二进制增强子搜索算法 (BRCSA),灵感来自于子的行为.
- 应用BRCSA用于使用二进制编码方案进行多式联运特征选择.
- 优化了特征选择过程,以提高分类模型性能.
主要成果:
- 基于BRCSA的方法在分类准确性方面明显超过了最先进的方法.
- 与现有算法相比,平均准确度提高了高达42%.
- 在基准数据集上的实验验证证证了该方法的有效性.
结论:
- 拟议的BRCSA算法是多式联运特征选择的高效方法.
- 实现了卓越的分类准确性,表明了对现实世界应用的巨大潜力.
- BRCSA提供了一个强大的解决方案,用于优化复杂数据集中的特征选择.
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