Gao Zhaohui, Mo Huan, Yan Zicheng, Fan Qinqin
Intelligent Transportation System Research Center, Southeast University, Nanjing 211189, China.
Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China.
Biomimetics (Basel). 2024 Oct 10;9(10):613. doi: 10.3390/biomimetics9100613.
To facilitate the intelligent classification of unmanned highway toll stations, selecting effective and useful features is pivotal. This process involves achieving a tradeoff between the number of features and the classification accuracy while also reducing the acquisition costs of features. To address these challenges, a multimodal multi-objective feature selection (MMOFS) method is proposed in the current study. In the MMOFS, we utilize a multimodal multi-objective evolutionary algorithm to choose features for the unmanned highway toll station classification model and use the random forest method for classification. The primary contribution of the current study is to propose a feature selection method specifically designed for the classification model of unmanned highway toll stations. Experimental results using actual data from highway toll stations demonstrate that the proposed MMOFS outperforms the other two competitors in terms of PSP, HV, and IGD. Furthermore, the proposed algorithm can provide decision-makers with multiple equivalent feature selection schemes. This approach achieves a harmonious balance between the model complexity and the classification accuracy based on actual scenarios, thereby providing guidance for the construction of unmanned highway toll stations.
为了促进无人公路收费站的智能分类，选择有效且有用的特征至关重要。这一过程涉及在特征数量与分类精度之间进行权衡，同时还要降低特征的获取成本。为应对这些挑战，本研究提出了一种多模态多目标特征选择（MMOFS）方法。在MMOFS中，我们利用多模态多目标进化算法为无人公路收费站分类模型选择特征，并使用随机森林方法进行分类。本研究的主要贡献在于提出了一种专门为无人公路收费站分类模型设计的特征选择方法。使用来自公路收费站的实际数据进行的实验结果表明，所提出的MMOFS在PSP、HV和IGD方面优于其他两个竞争对手。此外，所提出的算法可以为决策者提供多个等效的特征选择方案。这种方法基于实际场景在模型复杂性和分类精度之间实现了和谐平衡，从而为无人公路收费站的建设提供指导。
