一个系统的文献综述关于基于元启发的特征选择技术,用于文本分类
Sarah Abdulkarem Al-Shalif1, Norhalina Senan1, Faisal Saeed2
1Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia.
PeerJ. Computer science
|July 10, 2024
概括
特征选择 (FS) 通过识别关键数据特征来改善文本分类. 超启发式 (MH) 技术比传统方法更有效,对未来的应用有希望.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 特性选择 (FS) 对于优化数据科学应用,特别是文本分类至关重要.
- 过度和不相关的特征会对分类器的性能产生负面影响.
- 传统和元启发式 (MH) 技术用于FS.
研究的目的:
- 从2015-2022年开始,系统地分析文本分类中FS的MH技术.
- 识别和评估各种MH技术的优缺点.
- 将MH技术与传统的FS方法进行比较.
主要方法:
- 对108项初级研究进行了系统的文献审查.
- 专注于2015年至2022年间发表的研究.
- 搜索了包括Scopus,Science Direct和Google Scholar在内的数据库.
主要成果:
- 与传统的FS方法相比,MH技术显示出更高的性能.
- 确定了关键的MH技术及其相关优缺点.
- 突出了MH方法在特征选择中的效率和有效性.
结论:
- 在文本分类中,MH技术对于特征选择非常有效.
- 对MH技术的进一步研究,如环状海搜索 (RSS),可以增强FS的能力.
- MH技术为改进各种数据科学应用提供了巨大的潜力.
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