Article
Computer Science, Artificial Intelligence
Joseph A. Gallego, Fabio A. Gonzalez, Olfa Nasraoui
Summary: This paper explores the correspondence between least-square estimation in a reproducing kernel Hilbert space and different M-estimators in the original space, as well as the application of new robust kernels associated with various types of M-estimators in clustering tasks. The results show that some robust kernels perform as well as state-of-the-art robust clustering methods.
Article
Engineering, Electrical & Electronic
Xiao Lv, Wei Cui, Yulong Liu
Summary: Researchers propose a new algorithmic analysis for solving the precision matrix estimation problem in high-dimensional settings, demonstrating a time-data tradeoff in the process and providing numerical experiments to validate the theoretical results.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Multidisciplinary Sciences
Asma Halim, Irshad Ahmad Arshad, Abdullah Mohammed Alomair, Mohammed Ahmed Alomair
Summary: In survey sampling, obtaining reliable responses to sensitive issues is challenging. In this study, a hidden logit estimation method using randomized response techniques is proposed to improve estimation accuracy. The Huang model is found to be the best model for this hidden logit method.
Article
Mathematics
Wenjuan Li, Wenying Wang, Jingsi Chen, Weidong Rao
Summary: Sufficient dimension reduction (SDR) is a useful tool for nonparametric regression with high-dimensional predictors, but many existing SDR methods rely on certain assumptions about the distribution of predictors. In this study, inspired by Wang et al., we propose a novel and effective method that combines the aggregate method and the kernel inverse regression estimation. Our proposed approach accurately estimates the dimension reduction directions and substantially improves the exhaustivity of the estimates with complex models. It is not dependent on the arrangement of slices and reduces the influence of extreme values of the response. The method performs well in both numerical examples and a real data application.
Article
Engineering, Electrical & Electronic
Vitor Rosa Meireles Elias, Vinay Chakravarthi Gogineni, Wallace A. Martins, Stefan Werner
Summary: This paper introduces efficient algorithms for kernel regression over graphs, including batch-based and online strategies that utilize random Fourier features for complexity reduction. Experimental results demonstrate competitive performance and provide options for different trade-offs between performance and complexity in various scenarios.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Bulent Tutmez
Summary: Environmental investigation and modelling require case-based sampling techniques in various domains. A machine learning algorithm with robustness, transparency, accuracy, and reproducibility has been established for reaching targets. Testing showed that the algorithm outperforms conventional methods and can be recommended in environmental sciences and other disciplines with minor adaptations.
APPLIED SOFT COMPUTING
(2023)
Article
Energy & Fuels
Wei Zhang, Shaomei Zhang, Yongchen Zhang, Guang Xu, Huizong Mao
Summary: This article presents a new method for state estimation in energy management systems. The combination of kernel ridge regression and unscented Kalman filter improves the accuracy and robustness of the model in both normal and abnormal conditions.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Computer Science, Hardware & Architecture
Yifei Wang, Mert Pilanci
Summary: We propose a fast algorithm for computing the entire ridge regression regularization path in nearly linear time. Our method constructs a basis on which the solution of ridge regression can be computed instantly for any value of the regularization parameter. Consequently, linear models can be tuned via cross-validation or other risk estimation strategies with substantially better efficiency.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Tyler Buffington, James G. Scott, Ofodike A. Ezekoye
Summary: This study explores different spatial and sociodemographic models to predict residential fire counts in census tracts for 118 U.S. fire departments across 25 states. The research highlights the impact of socioeconomic factors on fire risk and proposes a Bayesian hierarchical Poisson regression model that improves prediction accuracy.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Article
Automation & Control Systems
Fouzi Douak, Noureddine Ghoggali, Rachid Hedjam, Mohamed Lamine Mekhalfi, Nabil Benoudjit, Farid Melgani
Summary: This work introduces a new algorithm that combines nonlinear kernel regressors with optimization based on a multi-objective genetic algorithm to improve techniques used in spectroscopic data regression analysis. The algorithm simultaneously optimizes multiple complementary objectives for better outlier detection.
JOURNAL OF CHEMOMETRICS
(2021)
Article
Engineering, Electrical & Electronic
Pucha Song, Haiquan Zhao, Yingying Zhu
Summary: This paper proposes a robust MD-APA algorithm based on the maximum correntropy criterion (MCC) to address the convergence issue caused by impulsive noise in a multitask network. The algorithm adopts a robust adaptive kernel width strategy to enhance estimation behavior and analyzes the convergence range of step-size and theoretical steady-state mean square deviation (MSD) of the whole network.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Computer Science, Artificial Intelligence
Eufrasio de A. Lima Neto, Paulo C. Rodrigues
Summary: SVD is a widely used algorithm for dimensionality reduction and principal component analysis, but it is not suitable for data contaminated with outlying observations. To overcome this limitation, a kernel robust SVD algorithm is proposed, which operates in the original space and applies a robust linear regression framework to obtain robust estimates for the singular values and singular vectors. Simulation results show that the proposed algorithm outperforms classical and robust SVD algorithms. The merits of the proposed algorithm are also illustrated in an image recovery application.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Physics, Multidisciplinary
Archismita Dalal, Amara Katabarwa
Summary: A universal fault-tolerant quantum computer is currently unavailable, but the development of near-term quantum algorithms like robust amplitude estimation (RAE) can optimize the performance of noisy intermediate-scale quantum (NISQ) computers and early fault tolerant (EFT) quantum computers. The lack of realistic error models has been a challenge for RAE, but we solve this by tailoring device noise using randomized compiling to generate an effective noise model that closely simulates the impact of device noise on RAE. By conducting noisy simulations, we demonstrate that our noise-tailored RAE algorithm can achieve improvements in bias and precision, even showing an advantage over standard estimation techniques on IBM's quantum computer ibmq_belem.
NEW JOURNAL OF PHYSICS
(2023)
Article
Automation & Control Systems
Francesco Farina, Giuseppe Notarstefano
Summary: This article introduces a class of novel distributed algorithms for solving stochastic big-data convex optimization problems, involving consensus steps and updates on decision variables. It discusses the convergence of dynamic consensus algorithm and the algorithm's convergence to the optimal cost in expected value. The algorithm is tested on synthetic and real text data, showing promising results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Information Systems
Xinxin Zhang, Ronggang Wang, Da Chen, Yang Zhao, Wen Gao
Summary: This paper proposes a novel blind motion deblurring method for blurred images including light streaks, reducing the influence of outliers on deconvolution and utilizing more information to estimate the blur kernel. Experimental results demonstrate the high accuracy of the algorithm in restoring both synthetic and real images.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Haijiao Xu, Changqin Huang, Dianhui Wang
KNOWLEDGE-BASED SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Hailiang Ye, Feilong Cao, Dianhui Wang
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Automation & Control Systems
Changqin Huang, Qionghao Huang, Dianhui Wang
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Computer Science, Information Systems
H. K. Zhang, Y. F. Wang, D. H. Wang, Y. L. Wang
INFORMATION SCIENCES
(2020)
Article
Automation & Control Systems
Ming Li, Dianhui Wang
Summary: The study extends original SCNs to 2DSCNs for fast building randomized learners with matrix inputs, showing good potential for image data analytics.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Review
Computer Science, Artificial Intelligence
Wenxuan Liu, Junhua Zhao, Dianhui Wang
Summary: This paper presents an initial discussion on the applications and advancements of big data mining in intelligent energy systems. It discusses applications such as load forecasting, integrated energy systems, and electricity market forecasting, as well as research problems that need further attention in the future.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Computer Science, Artificial Intelligence
Aijun Yan, Jingcheng Guo, Dianhui Wang
Summary: In this paper, a heterogeneous feature ensemble method is proposed for modeling furnace temperature in the process of municipal solid waste incineration. By constructing multiple base models and utilizing a negative correlation learning strategy, accurate prediction and control of furnace temperature are achieved.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Pengxin Tian, Kai Sun, Dianhui Wang
Summary: This study develops a soft-sensing technique using SCNs and NNG algorithm to infer difficult-to-measure variables with easy-to-measure variables in industrial processes. The proposed method consists of two stages: using SCNs for industrial data modeling and applying NNG algorithm for model optimization. Experimental results demonstrate that the proposed soft-sensor performs better in terms of prediction accuracy.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Automation & Control Systems
Yan Pan, Chang-Qin Huang, Dianhui Wang
Summary: Multiview clustering partitions data based on multiple perspectives to generate more meaningful clusters. This article proposes a multiview spectral clustering method based on robust subspace segmentation. The method constructs feature matrices, performs low rank and sparse decomposition, and utilizes spectral clustering to produce the final clusters. Experimental results demonstrate that the proposed method outperforms other state-of-the-art multiview clustering techniques on benchmark datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Weitao Li, Yali Deng, Meishuang Ding, Dianhui Wang, Wei Sun, Qiyue Li
Summary: This paper proposes an intelligent classification method based on self-attention learning features and stochastic configuration networks to tackle the issues in current industrial data classification models. By utilizing self-attention mechanism for feature extraction and SCNs for classifier design, the proposed method enhances the robustness of the classification model through fuzzy integral integration.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Automation & Control Systems
Jia Chen, Ming Zhong, Jianxin Li, Dianhui Wang, Tieyun Qian, Hang Tu
Summary: This article focuses on the "oversmoothing" problem in attributed network representation learning, proposing to evaluate a smoothing parameter based on network topological characteristics to adaptively smooth node attributes and structure information, resulting in robust and distinguishable node features.Extensive experiments show that this approach effectively preserves the intrinsic information of networks compared to state-of-the-art works on benchmark datasets with varying topological characteristics.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Civil
Yongfu Wang, Bingxin Ma, Dianhui Wang, Tianyou Chai
Summary: This paper focuses on the problem of prescribed tracking performance control for uncertain steer-by-wire (SbW) systems with input nonlinearity and the limitation of CAN bandwidth. It introduces an adaptive interval type-2 fuzzy logic system (IT2 FLS) to approximate the lumped model uncertainty and applies a switching event-triggering mechanism (ETM) to save communication resources. By combining the backstepping approach and barrier Lyapunov function techniques, a prescribed tracking performance control method is proposed for SbW systems, eliminating the need for initial values of state errors in controller design. Theoretical analysis shows that the tracking error can converge to the predefined residual set within a preset time, while the closed-loop system is semi-globally stable. Simulations and vehicle experiments are conducted to verify the effectiveness of the proposed control method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wenhua Jiao, Ruilin Li, Jianguo Wang, Dianhui Wang, Kuan Zhang
Summary: Rehabilitation training has shifted from therapies to strategies with remote assistance. This paper proposes two solutions, Bagging SCNs and Boosting SCNs, for activity recognition based on SCNs. Experimental results demonstrate that these methods have good performance in remote rehabilitation training.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Kang Li, Junfei Qiao, Dianhui Wang
Summary: This article presents an online self-learning stochastic configuration network that improves the continuous learning ability of SCNs for modeling nonstationary data streams. The network autonomously adjusts parameters and structure based on real-time arriving data streams, using recursive learning mechanism and sensitivity analysis. Experimental results demonstrate the potential of the proposed method for analyzing nonstationary data streams.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Wu Ai, Dianhui Wang
INFORMATION SCIENCES
(2020)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)