通过synsets表示的文本的维度减少的多目标进化优化
Iñaki Vélez de Mendizabal1,2, Vitor Basto-Fernandes2, Enaitz Ezpeleta1
1Electronics and Computing Department, Mondragon Unibertsitatea, Arrasate-Mondragón, Gipuzkoa, Spain.
PeerJ. Computer science
|June 22, 2023
概括
本研究引入了一种新的多目标优化方法,用于垃圾邮件过,使用进化算法来减少文本特征. 低损失方法通过战略性地删除不相关信息,显著提高了分类器的效率.
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:
- 垃圾邮件过面临着诸如多义和同义等语言复杂性所带来的挑战.
- 现有的方法与无关或令人困惑的单词扎,影响分类准确性.
- 在模型构建之前,预处理对于处理这些语言问题至关重要.
研究的目的:
- 为解决由语言现象引起的垃圾邮件过准确性的局限性.
- 引入功能减少作为一个多目标优化问题.
- 为了比较在垃圾邮件过中对文本表示的不同维度减小策略.
主要方法:
- 使用多目标进化算法 (MOEA) 来解决特征减少问题.
- 实施无损 (synset分组),低损 (synset分组+无关信息删除) 和有损 (无关信息删除) 的策略.
- 实验性比较无损和低损文本表示方案.
主要成果:
- 拟议的MOEA框架实际上将特征减少作为一个多目标问题来处理.
- 低损失的维度减少策略显示了分类器效率的显著改善.
- 实验结果验证了低损失方法对无损失方法的有效性.
结论:
- 作为一个多目标优化问题框架的特征减少为文本分类提供了一种灵活的方法.
- 低损失策略,平衡概括和信息相关性,对于垃圾邮件过特别有效.
- 这项工作提供了一种通过智能功能减少来提高垃圾邮件过器性能的新方法.
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