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Adversarial Training–Based Deep Imbalanced Learning
- Yuhang Tian
  Yuhang Tian
  [email protected]
  https://orcid.org/0000-0002-5105-1698
  Business School, Sichuan University, Chengdu 610064, China; and Technology Finance Key Laboratory of Sichuan Province, Sichuan University, Chengdu 610065, China
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- Jin Xiao
  Corresponding Author
  Jin Xiao
  [email protected]
  https://orcid.org/0009-0007-7591-1406
  Business School, Sichuan University, Chengdu 610064, China; and Technology Finance Key Laboratory of Sichuan Province, Sichuan University, Chengdu 610065, China
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- Lean Yu
  Lean Yu
  [email protected]
  https://orcid.org/0000-0002-8035-4938
  Business School, Sichuan University, Chengdu 610064, China; and Technology Finance Key Laboratory of Sichuan Province, Sichuan University, Chengdu 610065, China
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- Yanlin Jia
  Yanlin Jia
  [email protected]
  https://orcid.org/0000-0002-5065-6740
  School of Sciences, Southwest Petroleum University, Chengdu 610500, China
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- Jing Huang
  Jing Huang
  [email protected]
  https://orcid.org/0000-0002-6153-989X
  School of Public Administration, Sichuan University, Chengdu 610064, China
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- Xiaoyi Jiang
  Xiaoyi Jiang
  [email protected]
  https://orcid.org/0000-0001-7678-9528
  Faculty of Mathematics and Computer Science, University of Münster, D-48149 Münster, Germany
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- Shouyang Wang
  Corresponding Author
  Shouyang Wang
  [email protected]
  https://orcid.org/0000-0001-5773-998X
  School of Entrepreneurship and Management, ShanghaiTech University, Shanghai 201210, China; and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
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Published Online:September 9, 2026
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Abstract
Financial fraud detection is crucial in the banking and financial sectors, but it faces considerable challenges because of class imbalance in transaction data and the growing threat of adversarial attacks. These issues frequently undermine the ...
Landscape-Aware Hybrid Metaheuristic for Cross-Dock Door Assignment
- Wei Yang
  Wei Yang
  [email protected]
  https://orcid.org/0000-0002-7826-5629
  School of Economics and Management, Xidian University, Xi’an 710126, China
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- Yang Wang
  Corresponding Author
  Yang Wang
  [email protected]
  https://orcid.org/0009-0004-1496-1440
  School of Management, Northwestern Polytechnical University, Xi’an 710072, China
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- Abraham P. Punnen
  Abraham P. Punnen
  [email protected]
  https://orcid.org/0000-0002-3859-9229
  Department of Mathematics, Simon Fraser University Surrey, Surrey, British Columbia V3T 0A3, Canada
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- Rafael Martí
  Rafael Martí
  [email protected]
  https://orcid.org/0000-0001-7265-823X
  Departament d’Estadística i Investigació Operativa, Universitat de València, 46100 Valencia, Spain
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Published Online:September 8, 2026
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Abstract
The cross-dock door assignment problem (CDAP) is a critical challenge in optimizing cargo transshipment in logistics, where each inbound door serves multiple origins and each outbound door serves multiple destinations, subject to capacity constraints on ...
Integrating Data Cleaning and Robustification: A Study on Linear Regression
- Utku Tarık Bilgiç
  Corresponding Author
  Utku Tarık Bilgiç
  [email protected]
  https://orcid.org/0000-0002-6109-2581
  Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
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- Xiaoning Qian
  Xiaoning Qian
  [email protected]
  https://orcid.org/0000-0002-4347-2476
  Department of Electrical & Computer Engineering, Texas A&M University, College Station, Texas 77843
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- Bo Zeng
  Bo Zeng
  [email protected]
  https://orcid.org/0000-0002-8689-4281
  Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
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Published Online:August 27, 2026
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Abstract
In this study, we propose a general framework that integrates both optimistic and pessimistic optimization approaches in solving the regression problem to address outlier cleaning and robustification in a unified fashion. Although data cleaning aims to ...
Decision-Driven Regularization: A Blended Model for Learning and Optimization
- Gar Goei Loke
  Gar Goei Loke
  [email protected]
  https://orcid.org/0000-0003-1007-4575
  Department of Management and Marketing, Durham University Business School, Durham DH1 1SL, United Kingdom
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- Qinshen Tang
  Corresponding Author
  Qinshen Tang
  [email protected]
  https://orcid.org/0000-0002-9742-6563
  Nanyang Business School, Nanyang Technological University, Singapore
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- Yangge Xiao
  Yangge Xiao
  [email protected]
  https://orcid.org/0000-0001-7375-9825
  Faculty of Business and Economics, The University of Melbourne, Carlton, Victoria 3053, Australia
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- Xun Zhang
  Xun Zhang
  [email protected]
  https://orcid.org/0000-0002-3721-5107
  International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
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Published Online:August 25, 2026
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Abstract
In contextual optimization, the decision maker seeks optimal decisions to minimize a cost function that varies based on observed features. This context is common in many business applications ranging from on-demand delivery and retail operations to ...
Weighted Optimal Classification Forests
- Víctor Blanco
  Corresponding Author
  Víctor Blanco
  [email protected]
  https://orcid.org/0000-0002-7762-6461
  IMAG Institute of Mathematics, University of Granada, 18012 Granada, Spain
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- Alberto Japón
  Corresponding Author
  Alberto Japón
  [email protected]
  https://orcid.org/0000-0002-6614-4600
  IMUS Institute of Mathematics, University of Seville, 41004 Seville, Spain
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- Justo Puerto
  Justo Puerto
  [email protected]
  https://orcid.org/0000-0003-4079-8419
  IMUS Institute of Mathematics, University of Seville, 41004 Seville, Spain
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- Peter Zhang
  Peter Zhang
  [email protected]
  Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
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Published Online:August 21, 2026
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Abstract
This paper introduces weighted optimal classification forests (WOCFs), a new family of classifiers that takes advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers. We propose a novel mathematical optimization-...

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