多目标自适应粒子群优化,用于分类中的大规模特征选择
Chenyi Zhang1, Yu Xue1, Ferrante Neri2
1School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China.
International journal of neural systems
|February 14, 2024
概括
本研究介绍了一种多目标自适应粒子群优化 (MOSaPSO) 算法,以改进高维数据的特征选择. MOSaPSO有效地减少了特征和分类错误,超过了现有的方法,特别是随着数据复杂性的增加.
科学领域:
- 机器学习 机器学习
- 优化算法 优化算法
- 数据科学数据科学数据科学
背景情况:
- 特性选择 (FS) 对于提高在高维数据集上的机器学习性能至关重要.
- 现有的多目标进化算法 (MOEA) 在大规模多目标FS问题 (LSMOFSPs) 中因扩大解决空间和众多无关功能而扎在局部最佳停滞.
- 当前的MOEA经常使用单一候选解决方案生成策略 (CSGS),这对于不同的LSMOFSPs是低效的,并且参数调整是耗时的.
研究的目的:
- 解决LSMOFSPs中现有的MOEA的局限性.
- 提出一种新的算法,可以有效地处理大规模的多目标特征选择.
- 通过有效地减少特征维度和分类错误来提高学习算法的性能.
主要方法:
- 开发一个多目标自适应粒子群集优化 (MOSaPSO) 算法.
- 快速非主导分类方法的整合.
- 利用自适应机制与五种修改后的高效候选解决方案生成策略 (CSGSs) 结合起来,用于生成新的解决方案.
主要成果:
- 在十个实验数据集中,MOSaPSO有效地减少了特征的数量.
- 该算法在训练和测试集上显著降低了分类错误率.
- 与现有算法相比,MOSaPSO表现出优越的性能,性能增长随着数据集维度的增加而增加.
结论:
- 拟议的MOSaPSO算法为大规模的多目标特征选择问题提供了有效的解决方案.
- 莫萨普索克服了传统的MOEA所面临的局部最佳停滞和低效的搜索策略的挑战.
- 该算法的自适应性和多个CSGS有助于其强大的性能,特别是对于高维数据.
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