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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.

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PubMed
概括
此摘要是机器生成的。

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.

关键词:
缩小尺寸的缩小方式存在论词典 存在论词典语义信息是语义信息.监督的分类是监督的分类.文字分类 文本分类 文本分类

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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.