一个增强的算法,用于减少基于语义的特征在垃圾邮件过中的垃圾邮件过
María Novo-Lourés1,2,3, Reyes Pavón1,2,3, Rosalía Laza1,2,3
1CINBIO - Biomedical Research Centre, CINBIO, Vigo, Pontevedra, Spain.
PeerJ. Computer science
|August 15, 2024
概括
本研究引入了一种新的无损特征减少方法,使用本体词典. 它实现了更高的准确性和更低的计算成本,而不是文本分类的进化算法.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 信息检索 信息检索
背景情况:
- 在分类任务中,基于synset的文本表示非常受欢迎.
- 像WordNet和BabelNet这样的本体词典增强了这些表示.
- 以前的方法,如语义维度减少系统 (SDRS),通过结合语义相关的特征来减少维度.
研究的目的:
- 为基于synset的文本表示开发一种新的无损特征减少方案.
- 提高分类准确性,特别是减少假阳性误差.
- 与现有的进化算法相比,减少所需的计算资源.
主要方法:
- 利用本体词典中的信息来减少特征.
- 一个新的无损方案,它结合了基于训练数据中类同质性的synsets.
- 在三个数据集上进行实验验证,与两种基于优化的方法进行比较.
主要成果:
- 拟议的方法比基于优化的方法准确度略高,特别是在减少假阳性误差方面.
- 新方案显著降低了计算资源需求.
- 在多个数据集中证明有效性.
结论:
- 开发的无损特征减少方案为进化算法提供了一个计算效率高,准确的替代方案.
- 这种方法有效地利用本体词典来改进文本分类.
- 它为基于synset的文本表示减少维度提供了一个有希望的方向.
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