Mu Guangyu, Li Jiaxue, Liu Zhanhui, Dai Jiaxiu, Qu Jiayi, Li Xiurong
School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China.
Key Laboratory of Financial Technology of Jilin Province, Changchun 130117, China.
Biomimetics (Basel). 2025 Jan 10;10(1):41. doi: 10.3390/biomimetics10010041.
With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method's principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder-Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk.
随着互联网的发展，社交媒体平台在传播与危机相关的内容方面逐渐变得强大。识别与自然灾害相关的信息丰富的推文对救援行动有益。面对海量文本数据时，选择关键特征、降低计算成本并提高模型分类性能是一项重大挑战。因此，本研究基于包装法原理提出一种用于自然灾害推文分类特征选择的多策略改进黑翅鸢算法（MSBKA）。首先，通过利用增强型圆映射、整合分层反向学习并引入 Nelder-Mead 方法对黑翅鸢算法进行改进。然后，将 MSBKA 与优秀分类器 SVM（径向基核函数）相结合构建混合模型。最后，MSBKA-SVM 模型执行特征选择和推文分类任务。对来自四次自然灾害的数据进行实证分析表明，所提出的模型实现了 0.8822 的准确率。与遗传算法（GA）、粒子群优化算法（PSO）、正弦余弦算法（SSA）和黑翅鸢算法（BKA）相比，准确率分别提高了 4.34%、2.13%、2.94% 和 6.35%。本研究证明 MSBKA-SVM 模型能够在降低灾害风险方面发挥支持作用。
