Information Sciences最新文献_第2页

Consensus-based iterative meta-pseudo-labeling for deep semi-supervised learning 基于共识的深度半监督学习迭代元伪标记

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-20 DOI: 10.1016/j.ins.2024.121671

David Aparco-Cardenas, Jancarlo F. Gomes, Alexandre X. Falcão, Pedro J. de Rezende

A known issue that hinders the development of deep learning models is the need for accurate annotation of a large quantity of samples – a time-consuming, labor-intensive, and error-prone task. This limitation is particularly critical in areas where data annotation requires expert knowledge. Semi-supervised learning methods, such as pseudo-labeling, can alleviate the problem by capitalizing on both limited labeled and plentiful unlabeled data; nonetheless, state-of-the-art methods often require pre-trained encoders and validation sets to deliver effective solutions. Herein, we introduce a teacher-student-based iterative meta-pseudo-labeling approach, named consensus Deep Feature Annotation (cons-DeepFA), that enables the training of custom Convolutional Neural Networks (CNNs) from small quantities of labeled samples without reliance on pre-trained encoders and validation sets. cons-DeepFA explores Feature Learning from Image Markers (FLIM) to initialize the filters of a target CNN (student) from minimal data annotation – i.e., user-drawn markers on discriminative regions of a few selected images per class. During each of a few iterations, the latent space of the student's last dense layer is non-linearly projected onto a two-dimensional space for downstream label propagation via an optimum-connectivity-based approach (teacher); afterward, the student is re-trained using pseudo-labeled samples selected by the proposed consensus mechanism, which jointly improves the latent space, its projection, and the student's generalization ability as iterations progress. This strategy was recently introduced with pre-trained encoders by selecting the most confident pseudo-labeled samples to re-train the student. While building on previous methods, cons-DeepFA presents two key contributions. It (i) incorporates FLIM to enable training a custom CNN from scratch with faster convergence, improving its generalization ability, and (ii) introduces a consensus-based procedure over multiple iterations that selects more accurately pseudo-labeled samples for re-training the CNN. Lastly, cons-DeepFA is evaluated on five challenging biological image datasets, demonstrating its effectiveness and competitiveness when compared to seven state-of-the-art methods from four semi-supervised learning paradigms.

阻碍深度学习模型开发的一个已知问题是需要对大量样本进行准确标注，这是一项耗时、耗力且容易出错的任务。在数据标注需要专家知识的领域，这一限制尤为关键。伪标注等半监督学习方法可以利用有限的标注数据和大量的未标注数据来缓解这一问题；不过，最先进的方法通常需要预先训练编码器和验证集才能提供有效的解决方案。在此，我们介绍一种基于师生的迭代元伪标注方法，名为共识深度特征标注（consensus Deep Feature Annotation，简称cons-DeepFA），该方法可从少量标注样本中训练自定义卷积神经网络（CNN），而无需依赖预先训练的编码器和验证集、即用户在每类选定的几幅图像的判别区域上绘制标记。在每次迭代过程中，学生最后一个稠密层的潜在空间会通过一种基于最优连接性的方法（教师）非线性地投射到一个二维空间上，用于下游标签传播；之后，学生会使用由所提出的共识机制选择的伪标签样本进行再训练，随着迭代的进行，该机制会共同改善潜在空间、其投射以及学生的泛化能力。这种策略最近被引入到预训练编码器中，通过选择最有信心的伪标签样本来重新训练学生。Cons-DeepFA 以之前的方法为基础，做出了两大贡献。它(i)结合了 FLIM，从而能以更快的收敛速度从头开始训练自定义 CNN，提高其泛化能力；(ii)引入了基于共识的多次迭代程序，选择更准确的伪标签样本来重新训练 CNN。最后，cons-DeepFA 在五个具有挑战性的生物图像数据集上进行了评估，与来自四种半监督学习范式的七种最先进方法相比，证明了它的有效性和竞争力。

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引用次数: 0

Group search optimization-assisted deep reinforcement learning intelligence decision for virtual network mapping 虚拟网络映射的群搜索优化辅助深度强化学习智能决策

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-20 DOI: 10.1016/j.ins.2024.121664

Xiancui Xiao, Feng Yuan

Virtual network mapping (VNM), as a key technology in network virtualization, has received widespread attention due to its ability to instantiate network services on infrastructure. However, existing VNM technologies have drawbacks, such as poor dynamic mapping processes, single search strategies, and low resource utilization. In this end, we propose a novel group search optimization-assisted deep reinforcement learning (DRL) intelligence decision for virtual network mapping, GSRL-VNM. In this algorithm, we first formalize the deep reinforcement learning model of VNM and describe the dynamic characteristics of VNM process. Then, in order to effectively reduce resource fragmentation and improve the mapping success rate in VNM process, group search optimization (GSO), a swarm intelligent optimization algorithm with excellent global search ability, is utilized to assist deep reinforcement learning intelligent decision-making by improving convergence speed and optimal value. The simulation results show that the proposed GSRL-VNM algorithm outperforms the existing baseline algorithms in terms of acceptance rate, link pressure, long-term average cost, and average revenue.

虚拟网络映射（VNM）作为网络虚拟化的一项关键技术，因其能够在基础设施上实例化网络服务而受到广泛关注。然而，现有的虚拟网络映射技术存在动态映射过程差、搜索策略单一、资源利用率低等缺点。为此，我们提出了一种用于虚拟网络映射的新型群搜索优化辅助深度强化学习（DRL）智能决策--GSRL-VNM。在该算法中，我们首先形式化了虚拟网络映射的深度强化学习模型，并描述了虚拟网络映射过程的动态特征。然后，为了有效减少 VNM 过程中的资源碎片并提高映射成功率，利用具有出色全局搜索能力的蜂群智能优化算法--群搜索优化（GSO），通过提高收敛速度和最优值来辅助深度强化学习智能决策。仿真结果表明，所提出的 GSRL-VNM 算法在接受率、链路压力、长期平均成本和平均收益方面均优于现有的基线算法。

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引用次数: 0

Restoration after deterioration in interdependent infrastructure networks: A two-stage hybrid method with minimum network performance loss 相互依存的基础设施网络恶化后的恢复：网络性能损失最小的两阶段混合方法

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-20 DOI: 10.1016/j.ins.2024.121655

Yulong Li , Han Su , Baisong Yang , Jie Lin , Yinghua Shen , Guobin Wu

Restoration of interdependent infrastructure networks (IINs) relies on the information from deterioration, which is of great significance because IINs support the normal functioning of social productivity and life. However, existing research has not fully addressed the restoration after deterioration in IINs, which is not conducive to the timely elimination of the adverse effects of infrastructure deterioration. First, a unified model for IINs is innovatively devised by considering both functional and operational interdependencies between infrastructures. Second, a two-stage hybrid method that generates the optimal restoration strategies after deterioration in IINs is proposed. Specifically, in the first stage, a hidden Markov chain model for deterioration prediction is constructed, which is solved by the Expectation-Maximization (EM) algorithm. In the second stage, an objective programming model with minimum network performance loss for restoration optimization is developed, and the optimal strategy is obtained by the ant colony algorithm. Finally, a real-world case is used to validate the feasibility and effectiveness of the proposed method. The results show that this method is efficient and effective in finding optimal restoration strategy after deterioration in IINs. We also investigate the effects of initial restoration time and restoration resource grouping, which provide helpful decision guidance for real cases.

相互依存的基础设施网络（IINs）的修复依赖于劣化信息，这一点意义重大，因为 IINs 支撑着社会生产力和生活的正常运转。然而，现有研究并没有完全解决 IINs 恶化后的修复问题，这不利于及时消除基础设施恶化带来的不利影响。首先，通过考虑基础设施之间的功能和运营相互依存关系，创新性地设计了 IINs 的统一模型。其次，提出了一种两阶段混合方法，用于生成 IINs 恶化后的最佳修复策略。具体来说，在第一阶段，构建一个隐马尔科夫链模型用于劣化预测，并通过期望最大化（EM）算法进行求解。在第二阶段，为修复优化建立了一个网络性能损失最小的目标编程模型，并通过蚁群算法获得最优策略。最后，利用实际案例验证了所提方法的可行性和有效性。结果表明，该方法能有效地找到 IIN 恶化后的最优修复策略。我们还研究了初始修复时间和修复资源分组的影响，为实际案例提供了有益的决策指导。

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引用次数: 0

Improving stochastic models by smart denoising and latent representation optimization 通过智能去噪和潜在表示优化改进随机模型

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-19 DOI: 10.1016/j.ins.2024.121672

Jakob Jelenčič , M. Besher Massri , Ljupčo Todorovski , Marko Grobelnik , Dunja Mladenić

This paper introduces an innovative deep learning-based optimization method specifically designed for data derived from stochastic processes. Addressing the prevalent issue of rapid overfitting in real-world scenarios with limited historical data, our approach focuses on denoising optimization. The method effectively balances the simultaneous optimization of latent data representation and target variables, leading to enhanced model performance. We rigorously test our approach using five diverse real-world datasets. Our study is structured into three parts: an ablation study to validate the individual components of our method, a statistical analysis using the Wilcoxon rank-sum test to confirm the superiority of our method against five research hypotheses, and a detailed exploration of parameter visualization and fine-tuning. The comprehensive evaluation demonstrates that our method not only outperforms existing techniques but also significantly contributes to the advancement of deep learning models for stochastic processes. The findings underscore the potential of our method as a robust solution to the challenges in modeling stochastic processes with deep learning, offering new avenues for efficient and accurate predictions.

本文介绍了一种基于深度学习的创新优化方法，该方法专为随机过程衍生的数据而设计。针对现实世界中历史数据有限的情况下普遍存在的快速过拟合问题，我们的方法侧重于去噪优化。该方法有效地平衡了潜在数据表示和目标变量的同步优化，从而提高了模型性能。我们使用五个不同的真实世界数据集对我们的方法进行了严格测试。我们的研究分为三个部分：消融研究以验证我们方法的各个组成部分；使用 Wilcoxon 秩和检验进行统计分析，以确认我们的方法在五个研究假设中的优越性；以及对参数可视化和微调的详细探讨。综合评估结果表明，我们的方法不仅优于现有技术，而且极大地推动了随机过程深度学习模型的发展。这些发现强调了我们的方法的潜力，它是利用深度学习对随机过程建模所面临挑战的一种稳健的解决方案，为高效、准确的预测提供了新的途径。

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引用次数: 0

Preserving privacy in association rule mining using multi-threshold particle swarm optimization 利用多阈值粒子群优化在关联规则挖掘中保护隐私

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-19 DOI: 10.1016/j.ins.2024.121673

Shahad Aljehani , Youseef Alotaibi

Healthcare data has become a powerful resource for generating insights that drive medical research. Association Rule Mining (ARM) techniques are widely used to identify relationships among diseases, treatments, and symptoms. However, sensitive information is often exposed, creating significant privacy challenges, particularly when data is integrated from multiple sources. Although Privacy-Preserving Association Rule Mining (PPARM) methods have been developed to address these issues, most rely on a single, predefined Minimum Support Threshold (MST) that is inflexible in adapting to diverse rule patterns. In this study, a Multi-Threshold Particle Swarm Optimization for Association Rule Mining (MPSO4ARM) model is introduced, integrating the Apriori and Particle Swarm Optimization (PSO) algorithms to perform data mining while protecting sensitive rules. A novel approach is employed by the proposed model to dynamically adjust the MST, allowing for more adaptive and effective privacy preservation. The MPSO4ARM model adjusts the MST on-the-fly based on rule length, improving its ability to safeguard sensitive data across various datasets. The proposed model was evaluated on the Chess, Mushroom, Retail, and Heart Disease datasets. The experimental results showed that the MPSO4ARM model outperforms traditional Apriori and conventional PSO algorithms, achieving higher fitness values and reducing side effects such as Hiding Failure (HF) and Missing Cost (MC), particularly in the Heart Disease and Mushroom datasets. Although the dynamic MST function introduces a moderate increase in computational runtime compared to Apriori and conventional PSO, this trade-off between execution time and enhanced privacy protection is considered acceptable, given the model's substantial improvements in data utility and rule sanitization.

医疗保健数据已成为推动医学研究的强大洞察力资源。关联规则挖掘 (ARM) 技术被广泛用于识别疾病、治疗和症状之间的关系。然而，敏感信息往往会暴露出来，给隐私带来巨大挑战，尤其是当数据从多个来源整合时。虽然保护隐私的关联规则挖掘（PPARM）方法已被开发出来以解决这些问题，但大多数方法都依赖于单一的、预定义的最小支持阈值（MST），这种方法在适应各种规则模式方面缺乏灵活性。本研究引入了关联规则挖掘的多阈值粒子群优化（MPSO4ARM）模型，该模型整合了 Apriori 算法和粒子群优化（PSO）算法，在进行数据挖掘的同时保护敏感规则。该模型采用了一种新颖的方法来动态调整 MST，从而更自适应、更有效地保护隐私。MPSO4ARM 模型可根据规则长度实时调整 MST，从而提高了在各种数据集上保护敏感数据的能力。我们在国际象棋、蘑菇、零售和心脏病数据集上对所提出的模型进行了评估。实验结果表明，MPSO4ARM 模型优于传统的 Apriori 算法和传统的 PSO 算法，尤其是在心脏病和蘑菇数据集上，MPSO4ARM 模型获得了更高的适应度值，减少了隐藏失败（HF）和缺失成本（MC）等副作用。虽然与 Apriori 和传统 PSO 相比，动态 MST 函数会适度增加计算运行时间，但考虑到该模型在数据实用性和规则净化方面的显著改进，这种在执行时间和增强隐私保护之间的权衡是可以接受的。

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引用次数: 0

On the exploitation of control knowledge for enhancing automated planning 论利用控制知识加强自动规划

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-19 DOI: 10.1016/j.ins.2024.121666

Xu Lu , Bin Yu , Cong Tian , Chu Chen , Zhenhua Duan

Heuristic search provides an efficient way to automatically explore the state space in planning, while Control Knowledge (CK) also has the potential to significantly increase the performance of planners. Currently, most of the state-of-the-art planners primarily rely on sophisticated heuristic mechanisms. However, these planners fail to scale up and to provide (high-quality) solutions in a range of problems.

The objective of this paper is to incorporate CK with heuristic search in order to leverage the advantages of both, thus leading planners to achieve much higher efficiency. To achieve this, we introduce a novel CK which is specified by a variant of Linear Temporal Logic (LTL), referred to as LTL

_{P}

. We propose an encoding methodology that translates LTL

_{P}

into standard planning models. Consequently, we can directly use existing heuristic planners to solve the augmented problem, and avoid tailoring the planners in order to deal with CK implicitly. The novelty of this approach lies in that we define a useful CK LTL

_{P}

with a concise encoding methodology, that can significantly improve the efficiency of heuristic search. In this paper, the encoding process is formally presented, and theoretical results on the complexity and soundness of the encoding are strictly proved. We find that appropriate CK is a good complement to heuristic search, and is capable of making hard problems easy to solve. Experiments demonstrate that our approach shows highly competitive results versus heuristic search and other CK-based techniques on many intractable benchmark problems, benefiting in improving the coverage and quality of plans.

启发式搜索提供了一种在规划中自动探索状态空间的有效方法，而控制知识（CK）也有可能显著提高规划器的性能。目前，大多数最先进的规划器主要依赖于复杂的启发式机制。本文的目的是将控制知识与启发式搜索结合起来，以充分利用两者的优势，从而使规划器实现更高的效率。为此，我们引入了一种新型 CK，该 CK 由线性时态逻辑 (LTL) 的一种变体指定，称为 LTLP。我们提出了一种将 LTLP 转换为标准规划模型的编码方法。因此，我们可以直接使用现有的启发式规划器来解决增强问题，避免了为了隐式处理 CK 而对规划器进行定制。这种方法的新颖之处在于，我们用简洁的编码方法定义了有用的 CK LTLP，从而大大提高了启发式搜索的效率。本文正式介绍了编码过程，并严格证明了编码的复杂性和合理性的理论结果。我们发现，适当的 CK 是启发式搜索的良好补充，能够使难题变得容易解决。实验证明，在许多难以解决的基准问题上，我们的方法与启发式搜索和其他基于 CK 的技术相比，显示出极具竞争力的结果，在提高计划的覆盖率和质量方面大有裨益。

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引用次数: 0

An insightful data-driven crowd simulation model based on rough sets 基于粗糙集的数据驱动人群仿真模型

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-19 DOI: 10.1016/j.ins.2024.121670

Tomasz Hachaj, Jarosław Wąs

Data-driven crowd simulation with insightful principles is an open, real-world, and challenging task. The issues involved in modeling crowd movement so that agents' decision-making processes can be interpreted provide opportunities to learn about the mechanisms of crowd formation and dispersion and how groups cope with overcoming obstacles. In this article, we propose a novel agent-based simulation algorithm to infer practical knowledge of a problem from the real world by modeling the domain knowledge available to an agent using rough sets. As far as we know, the method proposed in our work is the first approach that integrates a well-established agent-based simulation model of social forces, an insightful knowledge representation using rough sets, and Bayes probability inference that models the stochastic nature of motion. Our approach has been tested on real datasets representing crowds traversing bottlenecks of varying widths. We also conducted a test on numerous artificial datasets involving 1,000 agents. We obtained satisfactory results that confirm the effectiveness of the proposed method. The dataset and source codes are available for download so our experiments can be reproduced.

利用具有洞察力的原理进行数据驱动的人群模拟是一项开放、现实和具有挑战性的任务。对人群运动进行建模以便解释代理的决策过程所涉及的问题，为我们提供了了解人群形成和分散机制以及群体如何克服障碍的机会。在本文中，我们提出了一种新颖的基于代理的模拟算法，通过使用粗糙集对代理可用的领域知识进行建模，从现实世界中推断问题的实用知识。据我们所知，我们在工作中提出的方法是第一种将成熟的基于代理的社会力量模拟模型、使用粗糙集的精辟知识表示法以及模拟运动随机性的贝叶斯概率推理整合在一起的方法。我们的方法已在代表穿越不同宽度瓶颈的人群的真实数据集上进行了测试。我们还在涉及 1000 个代理的大量人工数据集上进行了测试。我们获得了令人满意的结果，证实了所提方法的有效性。数据集和源代码可供下载，因此我们的实验可以重现。

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引用次数: 0

Enabling multi-step forecasting with structured state space learning module 利用结构化状态空间学习模块实现多步骤预测

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-19 DOI: 10.1016/j.ins.2024.121669

Shaoqi Wang, Chunjie Yang

Data-driven soft sensor incorporated with the model predictive control (MPC) algorithms facilitating product quality and cost control is of imperative importance in industrial processes. However, the widely used one-step forecasting method can not incorporate with MPC and therefore restricts the practical usage of soft sensor. Multi-step forecasting introduces long-term dependencies problems yet has not been effectively resolved within traditional model structure. To address this problem, this paper proposes the deep learning network architecture named Extended State Space Learning Module (ESSLM). ESSLM extends the nonlinear mapping architecture of deep learning based on state space and retains state transfer matrices to characterize the dynamics of the system. ESSLM distinguishes itself from explicit network architectures such as gated RNNs by addressing the long-term dependencies problems through an implicit initialization method, and the MLP and RNN algorithms can be regarded as the manifestation of ESSLM in special cases. ESSLM characterizes the latent space as the coefficients of the orthogonal basis functions so that the input data can be encoded into a high-dimensional feature space with minimal information loss which efficiently achieves multi-step forecasting and give greater utility and practical significance.

数据驱动的软传感器与模型预测控制（MPC）算法相结合，有助于产品质量和成本控制，这在工业流程中至关重要。然而，广泛使用的一步预测法无法与 MPC 相结合，因此限制了软传感器的实际应用。多步骤预测引入了长期依赖性问题，但在传统模型结构中尚未得到有效解决。为解决这一问题，本文提出了名为 "扩展状态空间学习模块（ESSLM）"的深度学习网络架构。ESSLM基于状态空间扩展了深度学习的非线性映射架构，并保留了状态转移矩阵来描述系统的动态特性。ESSLM 区别于门控 RNN 等显式网络架构，通过隐式初始化方法解决长期依赖性问题，MLP 和 RNN 算法可视为 ESSLM 在特殊情况下的体现。ESSLM 将潜空间表征为正交基函数的系数，这样就能以最小的信息损失将输入数据编码到高维特征空间中，从而有效地实现多步预测，具有更大的实用性和实际意义。

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引用次数: 0

Improving diversity and invariance for single domain generalization 提高单域泛化的多样性和不变性

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-19 DOI: 10.1016/j.ins.2024.121656

Zhen Zhang , Shuai Yang , Qianlong Dang , Tingting Jiang , Qian Liu , Chao Wang , Lichuan Gu

Single domain generalization aims to train a model that can generalize well to multiple unseen target domains by leveraging the knowledge in a related source domain. Recent methods focus on synthesizing domains with new styles to improve the diversity of training data. However, mainstream methods rely heavily on an additional generative model when generating augmented data, which increases optimization difficulties and is not conducive to generating diverse style data. Moreover, these methods do not sufficiently capture the consistency between the generated and original data when learning feature representations. To address these issues, we propose a novel single domain generalization method, namely DAI, which improves Diversity And Invariance simultaneously to boost the generalization capability of the model. Specifically, DAI consists of a style diversity module and a representation learning module optimized in an adversarial learning manner. The style diversity module uses a generative model, nAdaIN, to synthesize the data with significant style shifts. The representation learning module performs object-aware contrastive learning to capture the invariance between the generated and original data. Furthermore, DAI progressively synthesizes multiple novel domains to increase the style diversity of generated data. Experimental results on three benchmarks show the superiority of our method against domain shifts.

单域泛化的目的是利用相关源域的知识，训练出一个能很好地泛化到多个未见过的目标域的模型。最近的方法侧重于合成具有新风格的领域，以提高训练数据的多样性。然而，主流方法在生成增强数据时严重依赖额外的生成模型，这增加了优化难度，不利于生成多样化的风格数据。此外，这些方法在学习特征表征时不能充分捕捉生成数据和原始数据之间的一致性。为了解决这些问题，我们提出了一种新颖的单领域泛化方法，即 DAI，它能同时提高多样性和不变性，从而增强模型的泛化能力。具体来说，DAI 由风格多样性模块和以对抗学习方式优化的表征学习模块组成。风格多样性模块使用生成模型 nAdaIN 来合成具有显著风格偏移的数据。表征学习模块执行对象感知对比学习，以捕捉生成数据和原始数据之间的不变性。此外，DAI 还逐步合成多个新领域，以增加生成数据的风格多样性。在三个基准上的实验结果表明，我们的方法在应对领域偏移方面具有优势。

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引用次数: 0

A novel decision-making agent-based multi-objective automobile insurance pricing algorithm with insurers and customers satisfaction 基于代理决策的新型多目标汽车保险定价算法，兼顾保险公司和客户满意度

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2024-11-18 DOI: 10.1016/j.ins.2024.121665

Tao Ma , Li Guang Xie , Hong Zhao , Fang Yang , Chunsheng Liu , Jing Liu

Automobile insurance is essential for customers to obtain an appropriate automobile insurance policy and plays a significant role in the insurance industry. However, pricing a reasonable automobile insurance policy for different customers requires considering several indicators, such as the number of historical accidents, the frequency of long-distance driving, and the mileage driven per month. Therefore, how to make a reasonable automobile insurance pricing policy according to the needs of different customers is an urgent issue. Existing pricing methods often face the challenges of inefficiencies and mispricing, making it difficult to satisfy both insurers and customers. To better address this problem, this paper proposes a decision-making agent-based multi-objective automobile insurance pricing (DMA-MoAIP) algorithm. The DMA-MoAIP algorithm provides a diverse set of pricing strategies that meet various requirements, and considers the satisfaction of both insurers and customers. Firstly, a decision-making agent (DMA) framework is proposed for insurers, which provides an accurate assessment of the risk level for each customer. Secondly, a data-driven scoring (DDS) mechanism is established to better measure the satisfaction of insurers and customers and form a score that corresponds to their satisfaction. Thirdly, a novel multi-objective particle swarm optimization (MoPSO) algorithm is designed to search for effective and diverse solutions in dealing with automobile insurance pricing problem. Experimental results show that DMA-MoAIP outperforms five state-of-the-art multi-objective algorithms (including NSGA-II and so on) in terms of convergence and solution diversity. Specifically, the solution diversity of DMA- MoAIP in automotive pricing has increased by an average of 17%, which can provide more pricing options to meet different customer needs. In practical applications, DMA-MoAIP provides three distinct pricing strategies: insurer-oriented, customer-oriented, and compromise pricing. These strategies underscore the importance of considering relevant driving behavior metrics, rendering them valuable for real-life applications.

汽车保险是客户获得适当汽车保险的必要条件，在保险行业中发挥着重要作用。然而，为不同客户制定合理的汽车保险定价需要考虑多个指标，如历史事故次数、长途驾驶频率、每月行驶里程等。因此，如何根据不同客户的需求制定合理的汽车保险定价政策是一个亟待解决的问题。现有的定价方法往往面临着效率低下、定价失误等难题，难以同时满足保险公司和客户的需求。为了更好地解决这一问题，本文提出了一种基于决策代理的多目标汽车保险定价（DMA-MoAIP）算法。DMA-MoAIP 算法提供了一套满足不同要求的多样化定价策略，并同时考虑了保险公司和客户的满意度。首先，为保险公司提出了一个决策代理（DMA）框架，该框架能准确评估每个客户的风险水平。其次，建立了数据驱动评分（DDS）机制，以更好地衡量保险公司和客户的满意度，并形成与他们的满意度相对应的分数。第三，设计了一种新颖的多目标粒子群优化（MoPSO）算法，以寻找有效和多样化的解决方案来处理汽车保险定价问题。实验结果表明，DMA-MoAIP 在收敛性和解的多样性方面优于五种最先进的多目标算法（包括 NSGA-II 等）。具体来说，DMA-MoAIP 在汽车定价方面的解多样性平均提高了 17%，可以提供更多的定价选择，满足不同客户的需求。在实际应用中，DMA-MoAIP 提供了三种不同的定价策略：面向保险公司、面向客户和折中定价。这些策略强调了考虑相关驾驶行为指标的重要性，因此在实际应用中非常有价值。

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引用次数: 0