A Multimodal Multi-Objective Feature Selection Method for Intelligent Rating Models of Unmanned Highway Toll Stations.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Biomimetics Pub Date : 2024-10-10 DOI:10.3390/biomimetics9100613
Zhaohui Gao, Huan Mo, Zicheng Yan, Qinqin Fan
{"title":"A Multimodal Multi-Objective Feature Selection Method for Intelligent Rating Models of Unmanned Highway Toll Stations.","authors":"Zhaohui Gao, Huan Mo, Zicheng Yan, Qinqin Fan","doi":"10.3390/biomimetics9100613","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"9 10","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506808/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomimetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/biomimetics9100613","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
无人高速公路收费站智能评级模型的多模式多目标特征选择方法。
为促进无人值守高速公路收费站的智能分类,选择有效和有用的特征至关重要。在这一过程中,既要在特征数量和分类精度之间取得平衡,又要降低特征的获取成本。为了应对这些挑战,本研究提出了一种多模式多目标特征选择(MMOFS)方法。在 MMOFS 中,我们利用多模态多目标进化算法为无人高速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为高速公路无人收费站分类模型设计的特征选择方法。使用高速公路收费站实际数据的实验结果表明,所提出的 MMOFS 在 PSP、HV 和 IGD 方面优于其他两个竞争者。此外,所提出的算法还能为决策者提供多种等效的特征选择方案。这种方法在模型复杂性和基于实际场景的分类准确性之间实现了和谐平衡,从而为无人高速公路收费站的建设提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
期刊最新文献
Brain-Inspired Architecture for Spiking Neural Networks. Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection. Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm. Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems. Clinical Applications of Micro/Nanobubble Technology in Neurological Diseases.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1