Information Sciences最新文献_第5页

Hidden policy conditional attribute-based keyword search in federated learning framework 联邦学习框架中基于隐藏策略条件属性的关键字搜索

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-27 DOI: 10.1016/j.ins.2026.123150

Zerui Guo , Sha Ma , Qiong Huang

Nowadays, people are paying increasing attention to the security of personal privacy data in artificial intelligence systems. Federated learning is a solution to address the issue of unified data collection for training in artificial intelligence systems. Among these, the Cross-Sample computation process is a stage where personal privacy data is often leaked in federated learning, and a viable mechanism to ensure data confidentiality during the Cross-Sample computation process in federated learning is provided by attribute-based searchable encryption (ABSE). However, the search process in most existing ABSE systems is inherently sequential, which fundamentally precludes their use in scenarios demanding high throughput and concurrent execution. Meanwhile, it is worth noting that most ABSE schemes fail to achieve both rich attribute expression and hidden policy. In response to these limitations, the present work introduces Hidden Policy Conditional Attribute-Based Keyword Search (HP-CABKS), which supports server-side concurrent evaluation in the search phase. Under the Generic Group Model, our scheme satisfies adaptive security against chosen keyword attacks and keyword secrecy. Experimental results demonstrate that our scheme exhibits low time consumption in the search phase. Our scheme exhibits robust security, high efficiency, and strong practicality, making it well-suited for real-world applications such as Cross-Sample computation in federated learning.

如今，人们越来越关注人工智能系统中个人隐私数据的安全性。联邦学习是解决人工智能系统训练中统一数据收集问题的一种解决方案。其中，跨样本计算过程是联邦学习中个人隐私数据容易泄露的阶段，基于属性的可搜索加密（property -based searchable encryption， ABSE）为联邦学习跨样本计算过程中的数据保密性提供了一种可行的机制。然而，大多数现有ABSE系统中的搜索过程本质上是顺序的，这从根本上阻碍了它们在需要高吞吐量和并发执行的场景中的使用。同时，值得注意的是，大多数ABSE方案不能同时实现富属性表达和隐藏策略。为了应对这些限制，本工作引入了基于隐藏策略条件属性的关键字搜索（HP-CABKS），它支持搜索阶段的服务器端并发评估。在通用群模型下，我们的方案满足自适应安全性和关键字保密。实验结果表明，该方案具有较低的搜索耗时。我们的方案具有强大的安全性、高效率和很强的实用性，非常适合联邦学习中的交叉样本计算等实际应用。

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引用次数: 0

Interpretable structural modeling of MR images using q−Bézier curves: A geometry-aware paradigm beyond deep learning 使用q - bsamzier曲线的MR图像的可解释结构建模：一种超越深度学习的几何感知范式

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-27 DOI: 10.1016/j.ins.2026.123147

Faruk Özger , Aytuğ Onan , Nezihe Turhan , Zeynep Ödemiş Özger

Magnetic resonance (MR) imaging plays a critical role in diagnostic workflows, yet its reliability is frequently compromised by scanner-dependent bias, contrast variability, and intensity drift. Although deep learning methods achieve high performance, they generally require extensive supervision and demonstrate limited robustness across diverse clinical settings.

To address these challenges, we propose a transparent, geometry-aware framework for annotation-free MR enhancement based on

q

-Bézier curves. This model incorporates an adaptive deformation parameter

q (x)

that modulates local curvature, facilitating flexible adaptation to complex anatomical boundaries. The framework comprises three principal mechanisms: (i) adaptive

q (x)

for local responsiveness, (ii) monotone

q

-Bézier tone curves for intensity standardization, and (iii) Tikhonov-regularized optimization for smooth mapping. As a result, the operator remains interpretable, operates in linear time, and provides explicit control over smoothness.

The proposed approach was validated across five public cohorts (BraTS, ACDC, PROMISE12, fastMRI, IXI), demonstrating significant improvements in image fidelity (SSIM, CNR, NIQE) and downstream segmentation accuracy (Dice, HD95) relative to variational filters and state-of-the-art foundation models. Additionally, cross-vendor experiments confirm its robustness without the need for retraining. Collectively, these findings establish

q

-Bézier modeling as a principled, lightweight, and clinically interpretable alternative that complements deep learning by providing a geometry-aware pathway to robust MR representation.

磁共振成像在诊断工作流程中起着至关重要的作用，但其可靠性经常受到扫描仪相关偏差、对比度可变性和强度漂移的影响。尽管深度学习方法实现了高性能，但它们通常需要广泛的监督，并且在不同的临床环境中表现出有限的鲁棒性。为了解决这些挑战，我们提出了一个透明的、几何感知的框架，用于基于q- bsamizier曲线的无注释MR增强。该模型包含一个自适应变形参数q(x)，可调节局部曲率，促进对复杂解剖边界的灵活适应。该框架包括三个主要机制：(i)用于局部响应的自适应q(x), （ii）用于强度标准化的单调q- bzier音调曲线，以及（iii）用于平滑映射的tikhonov正则化优化。因此，操作符保持可解释性，在线性时间内操作，并提供对平滑性的显式控制。该方法在5个公共队列（BraTS、ACDC、PROMISE12、fastMRI、IXI）中得到验证，与变分滤波器和最先进的基础模型相比，在图像保真度（SSIM、CNR、NIQE）和下游分割精度（Dice、HD95）方面有了显著改善。此外，跨厂商实验证实了其鲁棒性，无需再训练。总的来说，这些发现确立了q- bsamzier建模作为一种有原则的、轻量级的、临床可解释的替代方案，通过提供一种几何感知的途径来实现鲁棒的MR表示，从而补充了深度学习。

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引用次数: 0

Global context modeling for image super-resolution transformer 图像超分辨率转换器的全局上下文建模

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-27 DOI: 10.1016/j.ins.2026.123135

Dongsheng Ruan , Yuan Zheng , Lide Mu , Ao Ran , Lei Pan , Mingfeng Jiang , Chengjin Yu , Nenggan Zheng , Huafeng Liu

Window-based Transformers have achieved remarkable results in image super-resolution (SR). State-of-the-art SR models generally employ a window self-attention mechanism combined with a multi-layer perceptron (MLP) to effectively capture long-range dependencies. However, the window design and the MLP’s deficiency in capturing spatial dependencies restrict their capacity to utilize global contextual information in images. This paper aims to address this limitation by introducing global context modeling. Specifically, we propose a general global context-injected framework for window self-attention. Within this framework, we develop a new instantiation with a novel global context-injected (GCI) module, which allows each window to take advantage of the contextual information from other windows. The GCI module is lightweight and can be easily integrated into existing window-based Transformers, improving performance with negligible increases in parameters and computational costs. Furthermore, we introduce a window self-attention (WSA) to vision state space (VSS) flow to further enhance the ability for global context modeling. We incorporate our advancements into popular SR models, such as SwinIR and SRFormer, creating enhanced versions. Extensive experiments on three representative SR tasks demonstrate the effectiveness of our methods, showing substantial performance improvements over their vanilla counterparts. Notably, our GCI-MSRformer outperforms current state-of-the-art models like MambaIR.

基于窗口的变形器在图像超分辨率（SR）方面取得了显著的成绩。最先进的SR模型通常采用窗口自注意机制结合多层感知器（MLP）来有效捕获远程依赖关系。然而，窗口设计和MLP在捕获空间依赖性方面的不足限制了它们利用图像中全局上下文信息的能力。本文旨在通过引入全局上下文建模来解决这一限制。具体来说，我们提出了一个通用的全局上下文注入框架，用于窗口自关注。在这个框架中，我们开发了一个新的实例化，使用一个新的全局上下文注入（GCI）模块，它允许每个窗口利用来自其他窗口的上下文信息。GCI模块重量轻，可以很容易地集成到现有的基于窗口的变压器中，在参数和计算成本几乎可以忽略不计的情况下提高性能。此外，在视觉状态空间流中引入窗口自关注（WSA），进一步增强了全局上下文建模的能力。我们将我们的进步纳入流行的SR模型，如SwinIR和SRFormer，创建增强版本。在三个具有代表性的SR任务上进行的大量实验证明了我们的方法的有效性，显示出与普通任务相比有实质性的性能改进。值得注意的是，我们的gci - msformer比MambaIR等当前最先进的机型性能更好。

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引用次数: 0

Multi-attribute group consensus decision-making with two-stage trust risk adjustment 基于两阶段信任风险调整的多属性群体共识决策

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-27 DOI: 10.1016/j.ins.2026.123162

Peide Liu , Yurong Qian , Ran Dang , Fei Teng , Peng Wang

As decision-making problems continue to expand, group decision-making (GDM) has seen growing interest in the application of social trust networks (STN). The trust capacity stems of a decision maker (DM) from both self-trust and external social support. Misalignment between the two may lead to trust crises or overconfidence, affecting decision outcomes. Under a two-dimensional linguistic (2DL) environment, a consensus method for multi-attribute group decision-making (MAGDM) that combines internal and external trust mechanisms is presented in this paper. First, DMs’ self-trust is assessed through subjective judgment in the 2DL setting, and then compared with social trust support from the STN in a two-stage trust risk evaluation to align individual competence with external expectations. Next, individual opinions are optimized during the consensus process while managing trust risk. In attribute assignment, individual weights are determined through the interaction between DMs and STN, and attribute weights are calculated using information entropy. To better capture DMs’ psychological behavior, such as regret and hesitation during comparisons, a new MAGDM ranking method integrating regret theory and the SIR method is proposed to improve decision reliability. Lastly, through an illustrative application in an actual data element market context and comparative analysis, the effectiveness of the proposed method is demonstrated, offering actionable insights to support decisions pertaining to data factor market development.

随着决策问题的不断扩大，群体决策（GDM）对社会信任网络（STN）的应用越来越感兴趣。决策者的信任能力来源于自我信任和外部社会支持。两者之间的不一致可能导致信任危机或过度自信，从而影响决策结果。在二维语言环境下，提出了一种结合内外信任机制的多属性群体决策共识方法。首先，通过主观判断在2DL环境下评估dm的自我信任，然后在两阶段信任风险评估中将其与STN的社会信任支持进行比较，以使个人能力与外部期望保持一致。其次，在共识过程中对个体意见进行优化，同时管理信任风险。在属性分配中，通过dm和STN之间的相互作用确定单个权重，并利用信息熵计算属性权重。为了更好地捕捉决策主体在比较过程中的后悔、犹豫等心理行为，提出了一种将后悔理论与SIR方法相结合的MAGDM排序方法，以提高决策的可靠性。最后，通过在实际数据要素市场背景下的说明性应用和比较分析，证明了所提出方法的有效性，为支持有关数据要素市场发展的决策提供了可操作的见解。

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引用次数: 0

Generalized mining of mixed drove co-occurrence patterns 混合驱动共现模式的广义挖掘

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-27 DOI: 10.1016/j.ins.2026.123148

Witold Andrzejewski, Pawel Boinski

In this paper, we investigate mining Mixed-Drove spatio-temporal Co-Occurrence Patterns (MDCOPs). MDCOPs represent sets of object types frequently located together for a given minimum fraction of time. Current solutions fail to address several important factors in practical applications. Specifically, state-of-the-art methods rely on a series of snapshots, i.e., discrete object positions recorded at predefined timestamps rather than their trajectories. However, spatio-temporal data gathering often depends on unsynchronized distributed sensors that independently register positions for each object.

To tackle this issue using traditional methods, one can interpolate object positions at snapshot timestamps. However, this raises another challenge: determining the optimal number of snapshots while balancing accuracy, processing time, and memory requirements. To overcome these limitations, we formulate a generalized MDCOP mining problem and introduce GMDCOP-Miner, an algorithm that employs a new, generalized time-prevalence measure. The proposed algorithm provides the most accurate results, equal to those obtained via state-of-the-art methods with the number of snapshots tending to infinity. Moreover, our experiments demonstrate that GMDCOP-Miner surpasses existing approaches in both processing time and memory efficiency.

本文研究了混合驱动时空共现模式（mdcop）的挖掘。mdcop表示在给定的最短时间内经常位于一起的对象类型集。目前的解决方案未能解决实际应用中的几个重要因素。具体来说，最先进的方法依赖于一系列快照，即在预定义的时间戳上记录的离散对象位置，而不是它们的轨迹。然而，时空数据的收集往往依赖于不同步的分布式传感器，这些传感器独立地记录每个物体的位置。要使用传统方法解决这个问题，可以在快照时间戳中插入对象位置。然而，这带来了另一个挑战：在平衡准确性、处理时间和内存需求的同时确定快照的最佳数量。为了克服这些限制，我们制定了一个广义MDCOP挖掘问题，并引入了GMDCOP-Miner，这是一种采用新的广义时间流行度量的算法。所提出的算法提供了最准确的结果，等于那些通过最先进的方法获得的快照数量趋于无穷大。此外，我们的实验表明，GMDCOP-Miner在处理时间和内存效率方面都优于现有的方法。

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引用次数: 0

Temporal knowledge graph completion via global structural representation and deep interaction 基于全局结构表示和深度交互的时间知识图谱完成

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-24 DOI: 10.1016/j.ins.2026.123139

Jingbin Wang, Yumeng Zhang, Zeyuan Lin, Jinsong Lai, Kun Guo

Temporal knowledge graphs (TKGs) comprise timestamped facts and are widely used in intelligent systems. However, large-scale TKGs are often incomplete, therefore Temporal Knowledge Graph Completion (TKGC) is an important task. Existing approaches mostly use local neighborhoods to learn entity and relation representations, ignoring query-aware global semantics and semantic linkages between quadruples. Furthermore, timestamps are frequently considered as independent features, ignoring their periodicity and interactions with the graph structure. We propose T-GRIN (Temporal Graph completion via Representation and INteraction) to incorporate query-aware global semantic representations and deep interaction between entities and relations. T-GRIN employs a dynamic time encoder to capture periodic temporal patterns, an entity encoder with relation-enhanced mechanisms to highlight query-relevant contexts, and a relation encoder with multi-head attention to model diverse semantics under temporal and entity contexts. Furthermore, an interactive convolutional decoder is designed to improve feature interaction and high-order semantic composition. Extensive experiments on benchmark datasets demonstrate the effectiveness of T-GRIN. In ICEWS05-15, T-GRIN outperforms the previous best model by 8.9% MRR and 10.9% Hit@1.

时间知识图（TKGs）由时间标记的事实组成，广泛应用于智能系统。然而，大规模的知识图谱往往是不完整的，因此时间知识图谱补全（TKGC）是一个重要的任务。现有的方法大多使用局部邻域来学习实体和关系表示，忽略了查询感知的全局语义和四元组之间的语义联系。此外，时间戳经常被认为是独立的特征，忽略了它们的周期性和与图结构的相互作用。我们提出了T-GRIN（通过表示和交互完成时态图）来结合查询感知的全局语义表示和实体和关系之间的深度交互。T-GRIN采用动态时间编码器捕获周期性时间模式，采用关系增强机制的实体编码器突出显示查询相关上下文，采用多头关注的关系编码器在时间和实体上下文中建模不同的语义。此外，设计了交互式卷积解码器，以改善特征交互和高阶语义组合。大量的基准数据集实验证明了T-GRIN的有效性。在ICEWS05-15中，T-GRIN的MRR比之前的最佳模型高出8.9%，Hit@1高出10.9%。

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引用次数: 0

Maximum-Value retinex decomposition guided generative priors for joint deraining and low-light image enhancement 基于最大值视差分解的生成先验联合训练与弱光图像增强

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-24 DOI: 10.1016/j.ins.2026.123136

Yanfei Sun , Junyu Wang , Rui Yin

Nighttime rainy conditions severely degrade visual quality in applications such as autonomous driving and aerial surveillance, where images suffer from compounded low-light and rain degradations. Diffusion models offer strong generative priors but face limitations in image restoration, including poor controllability, structural distortion, and domain gaps with degraded images. We present MR-SDformer, a novel framework that integrates Retinex-based decomposition with diffusion priors for joint nighttime deraining and low-light enhancement. The key innovation is the Maximum-Value Retinex decomposition, which isolates high-intensity rain streaks into the illumination map and produces a rain-free reflectance map that faithfully preserves intrinsic scene content. This decomposition not only bridges the gap between rainy inputs and rain-free priors but also provides complementary guidance to the generative process. Building on this, we design an asymmetric Hybrid Conditional Transformer that leverages the decomposed illumination and reflectance maps to condition the frozen diffusion model more effectively, enabling precise multi-modal feature fusion and high-fidelity reconstruction. Extensive experiments on both synthetic and real-world datasets confirm that MR-SDformer achieves state-of-the-art performance, delivering clearer structure, enhanced illumination, and more realistic visual quality under nighttime rainy conditions.

夜间多雨条件会严重降低自动驾驶和空中监视等应用的视觉质量，因为这些应用的图像会受到低光和雨水的双重影响。扩散模型提供了强大的生成先验，但在图像恢复方面存在局限性，包括可控性差、结构失真和退化图像的域间隙。我们提出了MR-SDformer，这是一种新颖的框架，将基于维甲酸的分解与扩散先验相结合，用于联合夜间脱模和弱光增强。关键的创新是Maximum-Value Retinex分解，它将高强度的雨条纹隔离到照明图中，并产生一个忠实地保留固有场景内容的无雨反射图。这种分解不仅弥补了有雨输入和无雨先验之间的差距，而且还为生成过程提供了补充指导。在此基础上，我们设计了一个非对称混合条件转换器，利用分解的光照和反射率映射更有效地调节冻结扩散模型，实现精确的多模态特征融合和高保真重建。在合成和真实数据集上进行的大量实验证实，MR-SDformer实现了最先进的性能，在夜间下雨条件下提供更清晰的结构，增强的照明和更逼真的视觉质量。

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引用次数: 0

Scalable and generalizable path planning for robotic navigation using transformer-based heuristic learning 基于变换的启发式学习的机器人导航路径规划

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-24 DOI: 10.1016/j.ins.2026.123149

Elie Thellier, Adolfo Perrusquía, Antonios Tsourdos

Efficient and scalable path planning is a critical challenge for autonomous robotic systems, particularly in complex real-world scenarios. Traditional heuristic search algorithms like A

^{*}

often struggle with scalability and adaptability in such environments. To address these limitations, we improve a search framework that integrates learned, instance-specific heuristics with conventional pathfinding techniques. Leveraging autoencoder transformer networks, we predict two key heuristic functions—Correction Factor (CF) and Path Probability Map (PPM)—trained on diverse datasets—the Motion Planning (MP) and Tiled-MP datasets—to cover a wide range of path planning scenarios. When integrated with Weighted A

^{*}

(WA

^{*}

) algorithm, this approach optimally solves 88% of MP instances, with paths averaging less than 0.7% longer than optimal, and requiring nearly five times fewer node expansions. The framework demonstrates the advantages of heuristic learning in handling larger path planning problems, with inference time accounting for just 10% of the total search duration. It solves nearly half of the most complex instances optimally, showcasing strong scalability for real-time robotics applications. The framework performs well in unseen environments, solving over 25% of new problems perfectly, finding near-optimal solutions with paths less than 7% longer than optimal, and requiring fewer than two-thirds of the typical expansions. Our framework outperforms learnable planners in both scalability and generalization.

高效和可扩展的路径规划是自主机器人系统面临的关键挑战，特别是在复杂的现实世界场景中。传统的启发式搜索算法（如A *）在这样的环境中经常与可伸缩性和适应性作斗争。为了解决这些限制，我们改进了一个搜索框架，该框架将学习的、特定于实例的启发式与传统的寻路技术集成在一起。利用自编码器变压器网络，我们预测了两个关键的启发式函数-校正因子（CF）和路径概率图(PPM) -在不同的数据集上训练-运动规划（MP）和平铺MP数据集-以覆盖广泛的路径规划场景。当与加权A∗(WA∗)算法集成时，该方法最优地解决了88%的MP实例，平均路径比最优时间长不到0.7%，并且需要的节点扩展几乎减少了五倍。该框架展示了启发式学习在处理较大路径规划问题方面的优势，推理时间仅占总搜索时间的10%。它最优地解决了近一半最复杂的实例，展示了实时机器人应用程序的强大可扩展性。该框架在不可见的环境中表现良好，完美地解决了超过25%的新问题，找到了接近最优的解决方案，比最优路径长不到7%，所需的扩展不到典型扩展的三分之二。我们的框架在可扩展性和泛化方面都优于可学习规划器。

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引用次数: 0

An adaptive differential evolution with deeply informed mutation strategy and historical information for numerical optimization 具有深度信息突变策略和历史信息的数值优化自适应差分进化

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-24 DOI: 10.1016/j.ins.2026.123146

Junyi Gou , Liangliang Sun , Jing Liu , Zhenghao Song , Ge Guo , She-Gan Gao , Ke Liu , Natalja Matsveichuk , Yuri Sotskov

Differential Evolution (DE), a population-driven stochastic optimization technique, has garnered significant interest among researchers across diverse disciplines because of its simple approach, high resilience, and few control parameters. However, numerous existing DE variants frequently encounter limitations when tackling intricate optimization problems, especially due to premature convergence weakness. To mitigate these deficiencies, the paper proposes an adaptive differential evolution with a deeply informed mutation strategy and historical information for numerical optimization (ADEDH), the main contributions of which can be outlined as follows: Firstly, a bi-stage parameter control strategy is proposed to achieve a better balance between exploration and exploitation. Secondly, a deeply informed mutation strategy is implemented, which uses the historical population to mirror the objective landscape and help guide the evolution. Thirdly, a diversity enhancement strategy based on historical information is proposed to tackle the premature convergence weakness. ADEDH is evaluated against nine outstanding competitors under a vast testing framework, containing CEC2013, CEC2014, and CEC2017 test suites. Additionally, the feasibility of ADEDH is further validated through its application to the parameter identification problem of a photovoltaic model. Experimental results demonstrate that ADEDH diversifies the population, attains superior solution precision, and achieves better stability.

差分进化（Differential Evolution， DE）是一种种群驱动的随机优化技术，因其方法简单、适应性强、控制参数少而引起了各学科研究者的极大兴趣。然而，许多现有的DE变体在处理复杂的优化问题时经常遇到限制，特别是由于过早收敛的弱点。为了解决这些问题，本文提出了一种具有深度信息突变策略和历史信息的数值优化自适应差分进化（ADEDH），主要贡献如下：首先，提出了一种双阶段参数控制策略，以实现勘探和开发之间的更好平衡。其次，采用深度信息突变策略，利用历史种群反映客观景观，帮助指导进化；第三，提出了一种基于历史信息的多样性增强策略，以克服过早收敛的缺点。ADEDH在包含CEC2013、CEC2014和CEC2017测试套件的庞大测试框架下，与九个优秀的竞争对手进行评估。此外，通过将ADEDH应用于光伏模型的参数辨识问题，进一步验证了该方法的可行性。实验结果表明，ADEDH使种群多样化，求解精度高，稳定性好。

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引用次数: 0

BPHD: Enterprise bankruptcy prediction with a hierarchical hypergraph and dual-decision experts 基于层次超图和双重决策专家的企业破产预测

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2026-01-23 DOI: 10.1016/j.ins.2026.123142

Boyuan Ren , Hongrui Guo , Hongzhi Liu , Xudong Tang , Jingming Xue , Zhonghai Wu

In today’s economic climate, enterprises face a variety of internal and external risks, making bankruptcy prediction critical for risk management. The traditional statistical and machine learning-based methods mainly rely on economic indicators, which are insufficient for deducing the risk propagation among enterprises. Recently, researchers have begun to explore the use of graph neural networks, utilizing their message-passing mechanisms to simulate the risk propagation process. However, existing graph-based methods often neglect degree imbalances, leading to high misjudgment rates for sparsely connected nodes. Furthermore, existing methods typically use a risk-oriented decision model to evaluate the likelihood of bankruptcy, which may lead to the overestimation of bankruptcy probabilities.

To address these issues, we propose a novel bankruptcy prediction model which consists of several key components, including a data-driven explicit risk encoding module, a global multihead attention-based implicit risk encoding module, a hierarchical hypergraph-based external risk enhancement module, and a dual-decision expert-based risk assessment module. We extend the traditional graph structure to a hierarchical hypergraph structure and design a corresponding information propagation strategy to alleviate the degree imbalance issue. Furthermore, a dual-decision assessment module is designed to integrate the perspectives of both risk and non-risk experts to prevent the overestimation of bankruptcy probabilities. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness of the proposed model, which achieves an accuracy of 77.64% and an AUC of 0.8270, significantly outperforming existing methods.

在当今的经济气候下，企业面临着各种各样的内部和外部风险，破产预测对于风险管理至关重要。传统的统计方法和基于机器学习的方法主要依赖于经济指标，不足以推断企业之间的风险传播。近年来，研究人员开始探索使用图神经网络，利用其消息传递机制来模拟风险传播过程。然而，现有的基于图的方法往往忽略度不平衡，导致对稀疏连接节点的误判率很高。此外，现有方法通常使用风险导向的决策模型来评估破产可能性，这可能导致对破产概率的高估。为了解决这些问题，我们提出了一种新的破产预测模型，该模型由几个关键组件组成，包括数据驱动的显式风险编码模块、基于全局多头注意的隐式风险编码模块、基于分层超图的外部风险增强模块和基于双决策专家的风险评估模块。我们将传统的图结构扩展为层次超图结构，并设计了相应的信息传播策略来缓解度不平衡问题。此外，设计了双重决策评估模块，以整合风险专家和非风险专家的观点，防止对破产概率的高估。在真实数据集上进行的大量实验证明了该模型的有效性，其准确率达到77.64%，AUC为0.8270，显著优于现有方法。

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引用次数: 0