使用基于演化优化的模糊反复神经网络的进化优化,SMS情绪分类
Ulligaddala Srinivasarao1, Aakanksha Sharaff1
1Department of Computer Science and Engineering, National Institute of Technology Raipur, Chhattisgarh, 492010 India.
概括
这项研究介绍了一种基于模糊的新型循环神经网络与哈里斯优化 (FRNN-HHO) 以改进垃圾邮件和火腿电子邮件的分类. FRNN-HHO模型通过进行分类后的情绪分析来提高准确性,在多个数据集中获得高AUC分数.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对于有效的电子邮件管理来说,对垃圾邮件和火腿邮件的分类至关重要.
- 现有的方法可以错误地将垃圾邮件分类为火腿,从而降低整体准确性.
- 情感分析提供了一个潜在的途径来改进消息分类.
研究的目的:
- 开发一个先进的架构,准确地对垃圾邮件和恶意消息进行分类.
- 通过将情绪分析集成到过程中来提高分类准确性.
- 引入基于模糊的循环神经网络,使用哈里斯霍克优化 (FRNN-HHO) 进行优化.
主要方法:
- 使用内核极端学习机器 (KELM) 分类器进行初始垃圾邮件和恶意消息分类.
- 实现了一个基于模糊的循环神经网络与哈里斯霍克优化 (FRNN-HHO) 进行分类后情绪分析.
- 使用标准指标评估性能,包括准确性,回忆,精度,F测量,RMSE和MAE.
主要成果:
- 拟议的FRNN-HHO架构在区分垃圾邮件和垃圾邮件方面表现出卓越的性能.
- 实现了高的曲线下面面积 (AUC) 值:SMS为0.9699;电子邮件为0.958;垃圾邮件杀手数据集为0.95.
- 通过解决错误分类,情绪分析集成显著提高了分类准确性.
结论:
- 通过情绪分析,FRNN-HHO模型有效地提高了垃圾邮件和火腿分类的准确性.
- 这种方法为改进文本挖掘和消息过系统提供了强大的解决方案.
- 该研究验证了FRNN-HHO在各种数据集中的有效性,突出了其实际适用性.
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