Article
Computer Science, Information Systems
Guoquan Li, Linxi Yang, Zhiyou Wu, Changzhi Wu
Summary: Proximal support vector machine (PSVM) is a variant of support vector machine (SVM) which aims to generate a pair of non-parallel hyperplanes for classification. Introducing l(0)-norm regularization in PSVM enables simultaneous selection of important features and removal of redundant features for classification. The proposed method utilizes a continuous nonconvex function and difference of convex functions algorithms (DCA) to solve the optimization problem efficiently.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Xinjiang Lu, Yunxu Bai
Summary: This article proposes a novel probabilistic LS-SVM method to enhance the modeling reliability of data contaminated by non-Gaussian noise. The effectiveness of the proposed method is demonstrated using both artificial and real cases.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Energy & Fuels
Iegor Riepin, Thomas Moebius, Felix Muesgens
Summary: This paper discusses the interdependence of electricity and natural gas markets, analyzing the challenges and uncertainties faced by complex energy systems. The research shows that considering uncertainties in integrated optimization problems can improve the quality of solutions.
Article
Computer Science, Information Systems
Chao Yuan, Liming Yang, Ping Sun
Summary: This work proposes a robust distance metric induced by correntropy based on Laplacian kernel, applies it to twin support vector machine classification, and builds a new robust algorithm to reduce noise and outliers. The DC algorithm converges linearly, showing robustness to noise and outliers in most cases.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Ruiyao Gao, Kai Qi, Hu Yang
Summary: The paper introduces the use of nonconvex loss functions in support vector machine (SVM) to improve robustness against noise. Two new loss functions, CaEN loss and HK loss, are proposed and applied in a fused robust geometric nonparallel SVM (FRGNHSVM). Experimental results show that FRGNHSVM achieves higher prediction accuracy, especially when dealing with label-contaminated datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zemin Zong, Xuewen Mu
Summary: A new second-order cone programming (SOCP) formulation is proposed in this study, inspired by the soft-margin linear programming support vector machine (LP-SVM) formulation and cost-sensitive framework. The proposed method maximizes slack variables for each class, relaxes the bounds on the VC dimension using the l(infinity)-norm, and penalizes them using corresponding regularization parametrization. It offers a flexible classifier extending the advantages of soft-margin LP-SVM to the second-order cone, and solves only two SOCP problems with second-order cone constraints, resulting in similar results to SOCP-SVM problem with less computational effort. Numerical experiments demonstrate that the proposed method outperforms conventional SOCP-SVM formulations and standard linear SVM formulations in terms of classification performance.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Jun Ma, Liming Yang, Qun Sun
Summary: Introducing robust distance metrics and a new loss function can improve the robustness of classification algorithms. The proposed adaptive robust twin support vector machine framework shows competitive advantages in experimentation with existing methods.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
M. Tanveer, Tarun Gupta, Miten Shah
Summary: This article introduces a new clustering algorithm pinTSVC to address the issues of noise sensitivity and re-sampling instability, by incorporating the pinball loss function for enhanced stability and performance in noise-corrupted datasets.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
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Energy & Fuels
Xueqian Fu
Summary: This paper introduces the application of statistical machine learning techniques to distribution network planning. By simulating various scenarios and uncertainties, the proposed model greatly improves planning performance while meeting accuracy requirements.
PROTECTION AND CONTROL OF MODERN POWER SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Abolfazl Hasanzadeh Shadiani, Mahdi Aliyari Shoorehdeli
Summary: This paper proposes an online approach for twin support vector machine, which utilizes recursive relation to avoid repetitive calculation of inverse matrices, resulting in improved training efficiency and maintained accuracy. Experimental results demonstrate the effectiveness of this method.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Jiangtao Zhao, Yuhong Sheng
Summary: This paper proposes a new SVM model by combining the uncertainty theory with the SVM theory to solve the problem of uncertain input data. Each uncertain data is regarded as an uncertain set, and a SVM model with uncertain chance constraints is established. The feasibility of the model is proved through numerical experiments using the Particle Swarm Optimization (PSO) algorithm.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Alfredo Marin, Luisa I. Martinez-Merino, Justo Puerto, Antonio M. Rodriguez-Chia
Summary: This paper introduces an exact method for a cost sensitive extension of the standard SVM, which outperforms classical models and previous heuristic solutions, especially when utilizing nonlinear kernel functions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Environmental Studies
Kamran Mostafaei, Shaho Maleki, Mohammad Zamani Ahmad Mahmoudi, Dariusz Knez
Summary: This research used Support Vector Machine (SVM) to predict the financial perspective of the Helichal granite mine in Iran for a duration of 30 years. The study collated financial data from the previous ten years of exploitation operations and created 100 simulations of net present value (NPV) using Monte Carlo technique. The results showed a high correlation (96%) between the SVM-predicted NPVs and the Monte Carlo-simulated NPVs, indicating the reliability of the SVM approach in anticipating the financial profitability of mining projects.
Article
Engineering, Environmental
Youzhi Wang, Ping Guo
Summary: A copula-measure based interval multi-objective multi-stage stochastic chance-constrained programming (CMIMOMSP) model is proposed for water consumption optimization. It introduces multi-objective programming to improve the traditional stochastic chance-constrained programming by considering relationships among various factors. The model is applied to a case study in the Heihe River Basin, showing different impacts of optimistic-pessimistic factors on water allocation for different sectors.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)