MSBKA:一种多策略改进的黑翼算法,用于对自然灾害推特进行特征选择和分类
Guangyu Mu1,2, Jiaxue Li1, Zhanhui Liu3
1School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China.
Biomimetics (Basel, Switzerland)
|January 24, 2025
概括
这项研究引入了一种用于自然灾害推特分类的新算法. 拟议的多策略改进的黑翼风算法 (MSBKA) 增强了特征选择,并提高了危机信息学的分类准确性.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算社会科学 计算社会科学
背景情况:
- 社交媒体平台对于在自然灾害期间传播危机信息至关重要.
- 有关灾难推特的有效识别对于高效的救援行动至关重要.
- 处理大量的文本数据在特征选择和分类准确性方面存在挑战.
研究的目的:
- 为自然灾害推特提出一个有效的功能选择方法.
- 提高社交媒体上与自然灾害有关的内容的分类性能.
- 为改进危机信息学开发一种混合模型.
主要方法:
- 为特征选择开发了一种多策略改进的黑翼风算法 (MSBKA).
- MSBKA结合了增强的Circle映射,层次反向学习和Nelder-Mead方法.
- 使用RBF内核实现了将MSBKA与Support Vector Machine (SVM) 结合在一起的混合模型.
主要成果:
- 在自然灾害推特分类上,MSBKA-SVM模型实现了0.8822的准确性.
- 与基因算法 (GA),粒子集群优化 (PSO) 和正弦共弦算法 (SSA) 相比,拟议的模型表现出更高的性能.
- 观察到精度提高了4.34% (GA),2.13% (PSO),2.94% (SSA) 和6.35% (BKA) 的情况.
结论:
- MSBKA-SVM模型为自然灾害推特的功能选择和分类提供了一个强大的解决方案.
- 这种方法可以通过改进与危机相关的社交媒体数据的分析来显著支持减少灾害风险的努力.
- 该研究强调了先进的元启发算法在增强危机信息系统方面的潜力.
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