最小平方双多类支向量机的稀疏解决方案使用l0和lp标准进行分类和特征选择
Hossein Moosaei1, Milan Hladík2
1Department of Informatics, Faculty of Science, Jan Evangelista Purkyně University, Ústí nad Labem, Czech Republic; Department of Econometrics, Prague University of Economics and Business, Czech Republic.
概括
本研究介绍了lp-norm最小平方双多类支向量机 (PLSTKSVC) 以改进多类分类和特征选择. 这种新的方法提高了分类准确性,并减少了高维数据集中的特征.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 存在多类分类挑战,特别是在高维数据方面.
- 像双 K 类支持向量分类 (Twin-KSVC) 和其改进 (ILSTKSVC) 等现有方法影响分类准确性,但往往忽视特征选择.
研究的目的:
- 为了提出一种新的方法,lp-norm最小平方双多类支持向量机 (PLSTKSVC),同时执行分类和特征选择.
- 通过将特征选择纳入多类分类框架来解决先前研究的局限性.
主要方法:
- 开发了PLSTKSVC,灵感来自ILSTKSVC和卡通性受约束优化,使用0
- 优化问题通过解决线性方程系统以获得近似解决方案来有效地解决.
- 在特定假设下研究最佳解决方案的特性.
主要成果:
- 拟议的PLSTKSVC方法在几个现实世界数据集中表现出卓越的性能.
- 与现有策略相比,实现了更高的分类准确性.
- 成功地减少了功能数量,表明了有效的功能选择.
结论:
- PLSTKSVC提供了一种强大而有效的方法,用于同时进行多类分类和特征选择.
- 该方法显示了涉及高维数据集的应用程序的巨大潜力,在这些应用程序中,精度和维度减少都至关重要.
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