用于运筹学和商业分析的集成学习

IF 4.5 3区管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Annals of Operations Research Pub Date : 2025-10-07 DOI:10.1007/s10479-025-06852-w

Koen W. De Bock, Matthias Bogaert, Philippe du Jardin

{"title":"用于运筹学和商业分析的集成学习","authors":"Koen W. De Bock, Matthias Bogaert, Philippe du Jardin","doi":"10.1007/s10479-025-06852-w","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This paper introduces the special issue on <i>Ensemble Learning for Operations Research and Business Analytics</i>. Its main purpose is to provide summaries for the 14 contributing research papers that were accepted for inclusion in this special issue. We first define an updated and extended taxonomy of ensemble learner architectures to characterize and differentiate ensemble learning algorithms. Subsequently, we characterize the special issue contributions in two ways: with respect to the operations research (OR) application they address and contribute to, and methodologically with respect to the newly defined taxonomy. Finally, we present an ambitious agenda for future research on ensemble learning for OR and business analytics.</p>\n </div>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":"353 2","pages":"419 - 448"},"PeriodicalIF":4.5000,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ensemble learning for operations research and business analytics\",\"authors\":\"Koen W. De Bock, Matthias Bogaert, Philippe du Jardin\",\"doi\":\"10.1007/s10479-025-06852-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>This paper introduces the special issue on <i>Ensemble Learning for Operations Research and Business Analytics</i>. Its main purpose is to provide summaries for the 14 contributing research papers that were accepted for inclusion in this special issue. We first define an updated and extended taxonomy of ensemble learner architectures to characterize and differentiate ensemble learning algorithms. Subsequently, we characterize the special issue contributions in two ways: with respect to the operations research (OR) application they address and contribute to, and methodologically with respect to the newly defined taxonomy. Finally, we present an ambitious agenda for future research on ensemble learning for OR and business analytics.</p>\\n </div>\",\"PeriodicalId\":8215,\"journal\":{\"name\":\"Annals of Operations Research\",\"volume\":\"353 2\",\"pages\":\"419 - 448\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Operations Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10479-025-06852-w\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Operations Research","FirstCategoryId":"91","ListUrlMain":"https://link.springer.com/article/10.1007/s10479-025-06852-w","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}

引用次数: 0

摘要

本文介绍了面向运筹学和商业分析的集成学习专题。其主要目的是为本特刊接受的14篇贡献研究论文提供摘要。我们首先定义了一个更新和扩展的集成学习器架构分类，以表征和区分集成学习算法。随后，我们以两种方式描述了特殊问题的贡献：关于它们所处理和贡献的运筹学（OR）应用，以及关于新定义的分类法的方法。最后，我们提出了一个雄心勃勃的未来研究议程，用于OR和业务分析的集成学习。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Ensemble learning for operations research and business analytics

This paper introduces the special issue on Ensemble Learning for Operations Research and Business Analytics. Its main purpose is to provide summaries for the 14 contributing research papers that were accepted for inclusion in this special issue. We first define an updated and extended taxonomy of ensemble learner architectures to characterize and differentiate ensemble learning algorithms. Subsequently, we characterize the special issue contributions in two ways: with respect to the operations research (OR) application they address and contribute to, and methodologically with respect to the newly defined taxonomy. Finally, we present an ambitious agenda for future research on ensemble learning for OR and business analytics.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Annals of Operations Research 管理科学-运筹学与管理科学

CiteScore

7.90

自引率

16.70%

发文量

596

审稿时长

8.4 months

期刊介绍： The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.