Neural Networks最新文献_第2页

UniSymNet: A Unified Symbolic Network with Sparse Encoding and Bi-level Optimization 具有稀疏编码和双级优化的统一符号网络。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-22 DOI: 10.1016/j.neunet.2026.108615

Xinxin Li , Juan Zhang , Da Li , Xingyu Liu , Jin Xu , Junping Yin

{"title":"UniSymNet: A Unified Symbolic Network with Sparse Encoding and Bi-level Optimization","authors":"Xinxin Li , Juan Zhang , Da Li , Xingyu Liu , Jin Xu , Junping Yin","doi":"10.1016/j.neunet.2026.108615","DOIUrl":"10.1016/j.neunet.2026.108615","url":null,"abstract":"<div><div>Automatically discovering mathematical expressions is a challenging issue to precisely depict natural phenomena, in which Symbolic Regression (SR) is one of the most widely utilized techniques. Mainstream SR algorithms target on searching for the optimal symbolic tree, but the increasing complexity of the tree structure often limits their performance. Inspired by neural networks, symbolic networks have emerged as a promising new paradigm. However, existing symbolic networks still face certain challenges: binary nonlinear operators { × , ÷} cannot be naturally extended to multivariate, training with fixed architecture often leads to higher complexity and overfitting. In this work, we propose a <strong>Uni</strong>fied <strong>Sym</strong>bolic <strong>Net</strong>work that unifies nonlinear binary operators into nested unary operators, thereby transforming them into multivariate operators. The capability of the proposed UniSymNet is deduced from rigorous theoretical proof, resulting in lower complexity and stronger expressivity. Unlike the conventional neural network training, we design a bi-level optimization framework: the outer level pre-trains a Transformer with sparse label encoding scheme to guide UniSymNet structure selection, while the inner level employs objective-specific strategies to optimize network parameters. This allows for flexible adaptation of UniSymNet structures to different data, leading to reduced expression complexity. The UniSymNet is evaluated on low-dimensional Standard Benchmarks and high-dimensional SRBench, and shows excellent symbolic solution rate, high fitting accuracy, and relatively low expression complexity.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108615"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emotion-Aware multimodal deepfake detection 情感感知多模态深度假检测。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-31 DOI: 10.1016/j.neunet.2026.108675

Teng Zhang , Gen Li , Yanhui Xiao , Huawei Tian , Yun Cao

{"title":"Emotion-Aware multimodal deepfake detection","authors":"Teng Zhang , Gen Li , Yanhui Xiao , Huawei Tian , Yun Cao","doi":"10.1016/j.neunet.2026.108675","DOIUrl":"10.1016/j.neunet.2026.108675","url":null,"abstract":"<div><div>With the continuous advancement of Deepfake techniques, traditional unimodal detection methods struggle to address the challenges posed by multimodal manipulations. Most existing approaches rely on large-scale training data, which limits their generalization to unseen identities or different manipulation types in few-shot settings. In this paper, we propose an emotion-aware multimodal Deepfake detection method that exploits emotion signals for forgery detection. Specifically, we design an emotion embedding extractor (Emoencoder) to capture emotion representations within modalities. Then, we employ Emotion-Aware Contrastive Learning and Cross-Modal Contrastive Learning to capture cross-modal inconsistencies and enhance modality feature extraction. Furthermore, we propose a Text-Guided Semantic Fusion module, where the text modality serves as a semantic anchor to guide audio-visual feature interactions for multimodal feature fusion. To validate our approach under data-limited conditions and unseen identities, we employ a cross-identity few-shot training strategy on benchmark datasets. Experimental results demonstrate that our method outperforms SOTAs and demonstrates superior generalization to both unseen identities and manipulation types.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108675"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Observer-based prescribed-time optimal neural consensus control for six-rotor UAVs: A novel actor-critic reinforcement learning strategy 基于观测器的六旋翼无人机规定时间最优神经共识控制：一种新的actor-critic强化学习策略。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-01 DOI: 10.1016/j.neunet.2026.108644

Yue Zhou , Liang Cao , Yan Lei , Hongru Ren

{"title":"Observer-based prescribed-time optimal neural consensus control for six-rotor UAVs: A novel actor-critic reinforcement learning strategy","authors":"Yue Zhou , Liang Cao , Yan Lei , Hongru Ren","doi":"10.1016/j.neunet.2026.108644","DOIUrl":"10.1016/j.neunet.2026.108644","url":null,"abstract":"<div><div>Six-rotor unmanned aerial vehicles (UAVs) offer significant potential, but still encounter persistent challenges in achieving efficient allocation of limited resources in dynamic and complex environments. Consequently, this paper explores the prescribed-time observer-based optimal consensus control problem for six-rotor UAVs with unified prescribed performance. A practical prescribed-time optimal control scheme is constructed through embedding the prescribed-time control method with a simplified reinforcement learning framework to realize the efficient resource allocation. Leveraging a prescribed-time adjustment function, the novel updating laws for actor and critic neural networks are developed, which guarantee that six-rotor UAVs reach a desired steady state within prescribed time. Moreover, an improved distributed prescribed-time observer is established, ensuring that each follower is able to precisely estimate the velocity and position information of the leader within prescribed time. Then, a series of nonlinear transformations and mappings is proposed, which cannot only satisfy diverse performance requirements under a unified control framework through only adjusting the design parameters a priori but also improve the user-friendliness of implementation and control design. Significantly, the global performance requirement simplifies verification process of initial constraints in traditional performance control methods. Furthermore, an adaptive prescribed-time filter is introduced to address the complexity explosion issue of the backstepping method on six-rotor UAVs, while ensuring the filter error converges within prescribed time. Eventually, simulation results confirm the effectiveness of the designed method.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108644"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentially private data augmentation via LLM generation with discriminative and distribution-aligned filtering 通过具有判别和分布对齐过滤的LLM生成差分私有数据增强。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.neunet.2026.108668

Yiping Song , Juhua Zhang , Zhiliang Tian , Taishu Sheng , Yuxin Yang , Minlie Huang , Xinwang Liu , Dongsheng Li

{"title":"Differentially private data augmentation via LLM generation with discriminative and distribution-aligned filtering","authors":"Yiping Song , Juhua Zhang , Zhiliang Tian , Taishu Sheng , Yuxin Yang , Minlie Huang , Xinwang Liu , Dongsheng Li","doi":"10.1016/j.neunet.2026.108668","DOIUrl":"10.1016/j.neunet.2026.108668","url":null,"abstract":"<div><div>Data augmentation (DA) is a widely adopted approach for mitigating data insufficiency. Conducting DA in private domains requires privacy-preserving text generation, including anonymization or perturbation applied to sensitive textual data. The above methods lack formal protection guarantees. Existing Differential Privacy (DP) learning methods provide theoretical guarantees by adding calibrated noise to models or outputs. However, the large output space and model scales in text generation require substantial noise, which severely degrades synthesis quality. In this paper, we transfer DP-based synthetic sample generation to DP-based sample discrimination. Specifically, we propose a DP-based DA framework with a large language model (LLM) and a DP-based discriminator for private-domain text generation. Our key idea is to (1) leverage LLMs to generate large-scale high-quality samples, (2) select synthesized samples fitting the private domain, and (3) align the label distribution with the private domain. To achieve this, we use knowledge distillation to construct a DP-based discriminator: teacher models, accessing private data, guide a student model to select samples under calibrated noise. A DP-based tutor further constrains the label distribution of synthesized samples with a low privacy budget. We theoretically analyze the privacy guarantees and empirically validate our method on three medical text classification datasets, showing that our DP-synthesized samples significantly outperform state-of-the-art DP fine-tuning baselines in utility.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108668"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lifelong knowledge graph embedding via diffusion model 基于扩散模型的终身知识图谱嵌入

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-21 DOI: 10.1016/j.neunet.2026.108630

Deyu Chen , Caicai Guo , Qiyuan Li , Jinguang Gu , Meiyi Xie , Hong Zhu

{"title":"Lifelong knowledge graph embedding via diffusion model","authors":"Deyu Chen , Caicai Guo , Qiyuan Li , Jinguang Gu , Meiyi Xie , Hong Zhu","doi":"10.1016/j.neunet.2026.108630","DOIUrl":"10.1016/j.neunet.2026.108630","url":null,"abstract":"<div><div>Lifelong knowledge graph embedding (KGE) methods aim to learn new knowledge continuously while retaining old knowledge. This line of work has received much attention for its potential to enable knowledge retention and transfer and to reduce training costs under knowledge graphs’ growing scale and flexibility. However, embedding space drift under different contexts is a crucial reason for catastrophic forgetting and inefficient learning of new facts, and existing work ignores this perspective. In order to address the above issues, we proposed a novel lifelong KGE framework that considers learning new facts and preserving old facts in a unified perspective. We propose a diffusion-based embedding method that captures the contextual variation of entity representations and obtains transferable embeddings. In order to handle the drift of the embedding space and balance the learning efficiency, we adopt a reconstruction and generation strategy based on contrastive learning. To avoid catastrophic forgetting and maintain the stability of the embedding distribution, we proposed an effective distribution regularization method. We conduct extensive experiments on seven benchmark datasets with different construction strategies and incremental speed. Experimental results show that our proposed framework outperforms existing lifelong KGE methods.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108630"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sonar-neus:voxel-based efficient neural implicit surface reconstruction for forward-looking sonar sonar - news：基于体素的高效神经隐式前视声纳表面重建。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.neunet.2026.108664

Shiji Qiu , Zuoqi Hu , Tiange Zhang , Zhi Liu , Junyu Dong , Qing Cai

{"title":"Sonar-neus:voxel-based efficient neural implicit surface reconstruction for forward-looking sonar","authors":"Shiji Qiu , Zuoqi Hu , Tiange Zhang , Zhi Liu , Junyu Dong , Qing Cai","doi":"10.1016/j.neunet.2026.108664","DOIUrl":"10.1016/j.neunet.2026.108664","url":null,"abstract":"<div><div>Dense 3D reconstruction using forward-looking sonar (FLS) is essential for ocean exploration. Recent advancements in FLS-based 3D reconstruction using neural radiance fields have emerged, demonstrating promising results. However, their excessively slow reconstruction speed significantly impacts their application in real-world scenarios, primarily due to two reasons: (1) the reliance on MLPs for scene representation leads to slow training, often requiring several hours for reconstruction; and (2) the uniform sampling strategy along the elevation arc is inefficient, greatly hindering both training speed and reconstruction quality. To address these challenges, we propose a voxel-based efficient neural implicit surface reconstruction approach using FLS, featuring three key innovations: 1) Replacing MLPs with voxel grids for scene representation, utilizing a signed distance function (SDF) voxel grid to model geometry and a feature voxel grid to capture appearance. 2) Introducing a hierarchical sampling strategy along the elevation arc to improve sampling efficiency. 3) Applying SDF Gaussian convolution to the SDF voxel grid, effectively reducing noise and surface roughness. Extensive experiments demonstrate that our method significantly outperforms existing unsupervised dense FLS reconstruction techniques. Notably, our approach achieves the same reconstruction quality in just 10 minutes of training that previously required 4 hours with state-of-the-art methods, while also delivering superior results. We will open-source our code upon paper acceptance.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108664"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learnable dendrite neural P systems and applications in survival prediction of glioblastoma patients 可学习树突神经P系统及其在胶质母细胞瘤患者生存预测中的应用。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-29 DOI: 10.1016/j.neunet.2026.108660

Xiu Yin , Xiyu Liu , Shulei Chang , Bosheng Song , Guanzhong Gong , Jiaxing Yin , Dengwang Li , Jie Xue

{"title":"Learnable dendrite neural P systems and applications in survival prediction of glioblastoma patients","authors":"Xiu Yin , Xiyu Liu , Shulei Chang , Bosheng Song , Guanzhong Gong , Jiaxing Yin , Dengwang Li , Jie Xue","doi":"10.1016/j.neunet.2026.108660","DOIUrl":"10.1016/j.neunet.2026.108660","url":null,"abstract":"<div><div>Current neural-like P systems use “point neurons” as the computing entities, and the computations in these neurons are simplified, ignoring the fact that, in organisms, subcellular compartments (such as neuronal dendrites) can also perform operations as independent computing units in addition to computing at the individual neuron level. The nervous system has a strong ability for optimization learning. Therefore, we propose learnable dendrite neural P (LDNP) systems with new plasticity rules, in which the dendrite structure and learning function can be adaptively changed when solving different application problems. Specifically, the dendrites of neurons are designed as dendritic trees composed of multiple dendritic branches, each of which serves as an independent computing unit. The multilevel complex topological structure of dendrites provides powerful computing capabilities for neurons. A model for predicting the overall survival of glioblastoma (GBM) patients was developed based on LDNP systems and validated on the GBM cohort from the Cancer Genome Atlas. Compared with thirteen state-of-the-art methods, the LDNP system achieves the best performance.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108660"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146127098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A cortico-cerebellar neural model for task control under incomplete instructions 不完全指令下任务控制的皮质-小脑神经模型。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-01-27 DOI: 10.1016/j.neunet.2026.108648

Lanyun Cui , Ying Yu , Qingyun Wang , Guanrong Chen

{"title":"A cortico-cerebellar neural model for task control under incomplete instructions","authors":"Lanyun Cui , Ying Yu , Qingyun Wang , Guanrong Chen","doi":"10.1016/j.neunet.2026.108648","DOIUrl":"10.1016/j.neunet.2026.108648","url":null,"abstract":"<div><div>Cerebellar-inspired motor control systems have been widely explored in robotics to achieve biologically plausible movement generation. However, most existing models rely heavily on high-dimensional instruction inputs during training, diverging from the input-efficient control observed in biological systems. In humans, effective motor learning often based on sparse or incomplete external feedback. It is possibly attributed to the interaction between multiple brain regions, especially the cortex and the cerebellum. In this study, we present a hierarchical cortico-cerebellar neural network model that investigates the neural mechanisms enabling motor control under incomplete or low-dimensional instructions. The evaluation results, measured by two complementary levels of evaluation metrics, demonstrate that the cortico-cerebellar model reduces dependency on external instruction without compromising trajectory smoothness. The model features a division of roles: the cortical network handles high-level action selection, while the cerebellar network executes motor commands by torque control, directly operating on a planar arm. Additionally, the cortex exhibits enhanced exploration indirectly driven by the stochastic characteristics of cerebellar torque control. Our results show that cortico-cerebellar coordination can facilitate robust and flexible control even with sparse instruction signals, suggesting a potential mechanism by which biological systems achieve efficient behavior under informational constraints.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108648"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structure-missing graph-level clustering network 缺少结构的图级聚类网络。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-02 DOI: 10.1016/j.neunet.2026.108682

Tianyu Hu , Renda Han , Liu Mao , Jing Chen , Xia Xie

{"title":"Structure-missing graph-level clustering network","authors":"Tianyu Hu , Renda Han , Liu Mao , Jing Chen , Xia Xie","doi":"10.1016/j.neunet.2026.108682","DOIUrl":"10.1016/j.neunet.2026.108682","url":null,"abstract":"<div><div>Graph-level clustering aims to group graphs into distinct clusters based on shared structural characteristics or semantic similarities. However, existing graph-level clustering methods generally assume that the input graph structure is complete and overlook the problem of missing relationships that commonly exist in real-world scenarios. These unmodeled missing relationships will lead to the accumulation of structural information distortion during the graph representation learning process, significantly reducing the clustering performance. To this end, we propose a novel method, Structure-Missing Graph-Level Clustering Network (SMGCN), which includes a structure augmentation module LR-SEA, an Anchor Positioning Mechanism, and Joint Contrastive Optimization. Specifically, we first output augmented graphs based on low-rank matrix completion, perform cluster matching using the Hungarian algorithm to obtain anchors, and then force same clustering graphs to converge to the corresponding anchors in the embedding space. According to our research, this is the first time that the graph-level clustering task with missing relations is proposed, and the superiority of our method is demonstrated through experiments on five benchmark datasets, compared with the state-of-the-art methods. Our source codes are available at <span><span>https://github.com/MrHuSN/SMGCN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108682"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Loyalty-SMOTE: Data synthesis algorithm for effective imbalanced data classification loyty - smote：一种有效的不平衡数据分类的数据综合算法。

IF 6.3 1区计算机科学

Neural Networks Pub Date : 2026-07-01 Epub Date: 2026-02-03 DOI: 10.1016/j.neunet.2026.108677

Shengquan Hu , Junfei Li , Zefeng Li , Zihao Zhang , Yan Feng , K. L Eddie Law

{"title":"Loyalty-SMOTE: Data synthesis algorithm for effective imbalanced data classification","authors":"Shengquan Hu , Junfei Li , Zefeng Li , Zihao Zhang , Yan Feng , K. L Eddie Law","doi":"10.1016/j.neunet.2026.108677","DOIUrl":"10.1016/j.neunet.2026.108677","url":null,"abstract":"<div><div>Imbalanced datasets are always problematic in training machine learning models, so that classifiers often struggle to achieve satisfactory performance. Numerous approaches have been developed to tackle imbalanced data problems. Among them, some data-level methods perform linear interpolations between neighboring minority class samples to generate new data points, while others focus on oversampling boundary samples which are specific to certain classes. However, many methods fail to consider scenarios involving noise susceptibility. In this paper, we propose a novel data-level method called the <em>Loyalty-SMOTE</em> algorithm. We introduce the concept of <em>Loyalty</em> to identify noise and boundaries within datasets. After identifying potential noisy datapoints, SMOTE (Synthetic Minority Oversampling Technique) algorithm is applied to oversample the minority class boundary data. Subsequently, a denoising process based on Loyalty is conducted to obtain a balanced dataset. To extend our design, the concept of <em>Attraction</em> is introduced to generalize the denoising technique for multiclass problems. In our study, the SVM (Support Vector Machine) classifier is used as our base learner,and extensive experiments are performed to evaluate and compare different algorithms. Our results demonstrate that Loyalty-SMOTE achieved superior performance across multiple metrics on both binary and multiclass UCI datasets. For 30 binary datasets, it achieved the highest scores in 26 datasets (87%) for F1-score, 29 datasets (97%) for AUROC, 26 datasets (87%) for recall, and 27 datasets (90%) for G-mean. For 5 multiclass datasets, our design achieved scores of 0.8317, 0.6153, 0.8537, and 0.6717, respectively.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"199 ","pages":"Article 108677"},"PeriodicalIF":6.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0