Article
Engineering, Electrical & Electronic
Dunbiao Niu, Chengjing Wang, Peipei Tang, Qingsong Wang, Enbin Song
Summary: This paper proposes a highly efficient sparse semismooth Newton (SsN) based augmented Lagrangian (AL) method for solving large-scale SVMs. The method utilizes the piecewise linear-quadratic structure of the problem and the sparse structure of the generalized Jacobian to achieve accurate and efficient solutions. Numerical experiments demonstrate that the proposed algorithm outperforms the current state-of-the-art solvers.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoming Wang, Shitong Wang, Zengxi Huang, Yajun Du
Summary: This paper introduces a novel method called sparse support vector machine guided by radius-margin bound (RMB-SSVM) to efficiently condense the basis vectors in support vector machines. By selecting basis vectors and learning corresponding coefficients with a criterion related to SVM's generalization ability, the RMB-SSVM model can yield better performance.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Sambhav Jain, Reshma Rastogi
Summary: This paper proposes Parametric non-parallel support vector machines for binary pattern classification. The model brings noise resilience and sparsity by intelligently redesigning the Support vector machine optimization. The experimental results validate its scalability for large scale problems.
Article
Computer Science, Information Systems
Sebastian Maldonado, Julio Lopez, Carla Vairetti
Summary: The predictive performance of classification methods relies heavily on the nature of the environment and dataset shift issue. A novel Fuzzy Support Vector Machine strategy is proposed in this paper to improve performance by redefining the loss function and applying aggregation operators to deal with dataset shift. Our methods outperform traditional classifiers in terms of out-of-time prediction using simulated and real-world dataset for credit scoring.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Hardware & Architecture
Tawfeeq Shawly, Ali Elghariani, Jason Kobes, Arif Ghafoor
Summary: This article introduces the concept of interleaving attacks and investigates how Hidden Markov Models can be used to detect and track these attacks. Two architectures are proposed to address the stealth nature of interleaving attacks, and their effectiveness is validated through comprehensive performance evaluation metrics.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Computer Science, Information Systems
Nalam Venkata Abhishek, Mohan Gurusamy
Summary: This letter discusses the potential attack in vehicular networks, proposes a mechanism to detect jammers in the network, and generates jointly sufficient data statistics through training data, demonstrating the effectiveness of the proposed detection system.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2021)
Article
Chemistry, Analytical
Simone Carone, Giovanni Pappalettera, Caterina Casavola, Simone De Carolis, Leonardo Soria
Summary: Machine learning techniques combined with machine condition monitoring can diagnose faults more accurately than other condition-based monitoring approaches. However, statistical or model-based methods are often not applicable in highly customized industrial environments. Monitoring the health of bolted joints is crucial for maintaining structural integrity. Yet, there is little research on detecting bolt loosening in rotating joints.
Article
Engineering, Electrical & Electronic
Lijuan Peng, Aijun Yin, Wei Song, Wenjie Yao, Hongji Ren, Linqiang Yang
Summary: The paper proposes a method for tracking physical conditions based on sleep monitoring, analyzing physical features using Hidden Markov Model and establishing a reference model for long-term tracking of variations.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Wangyong Lv, Tingting Li, Huali Ren, Shijing Zeng, Jiao Zhou
Summary: The IDH-MSVM algorithm adjusts the distance between hyperplanes and classical margins to handle multiclassification problems more flexibly. Experimental results on UCI standard data sets show that this method achieves better classification accuracy for multiclass data compared to other algorithms.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Mathematics
Roberto Barcenas, Maria Gonzalez-Lima, Joaquin Ortega, Adolfo Quiroz
Summary: The effectiveness of subsampling methods in reducing the required instances in the training stage of using support vector machines (SVMs) for classification in big data scenarios is explored in this paper with theoretical results. The main theorem states that, under certain conditions, a feasible solution can be found for the SVM problem using a randomly chosen subsample, which can be as close as desired to the classifier trained with the complete dataset in terms of classification error. Additionally, a new subsampling method called importance sampling and bagging is proposed, which provides a faster solution to the SVM problem without significant loss in accuracy compared to existing techniques.
Article
Engineering, Electrical & Electronic
Zhao Wu, Chao Wang, Huaiqing Zhang, Wenxiong Peng, Weihua Liu
Summary: This paper introduces the application of Hidden Markov Model (HMM) and its variations in Non-Intrusive Load Monitoring (NILM). By proposing a time-efficient Factorial Hidden Semi-Markov Model (TE-FHSMM), the paper achieves a reduction in time consumption while maintaining performance when dealing with datasets with different numbers of appliances. Additionally, experiments show that TE-FHSMM outperforms six state-of-the-art algorithms in terms of Accuracy and F1 score in real-world scenarios and publicly available datasets.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Computer Science, Theory & Methods
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie
Summary: The research shows that with a new defense algorithm and metric method, SVMs can improve resistance against targeted attacks and significantly reduce classification error rates.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
Article
Computer Science, Artificial Intelligence
H. Moosaei, S. Ketabchi, M. Razzaghi, M. Tanveer
Summary: In this paper, two efficient approaches of twin support vector machines (TWSVM) are proposed, including reformulating the formulation by introducing different norms and presenting an efficient algorithm using the generalized Newton's method. Experimental results demonstrate that the new methods outperform baseline methods in terms of performance and computational time.
NEURAL PROCESSING LETTERS
(2021)
