Article
Computer Science, Artificial Intelligence
Tong Gao, Hao Chen
Summary: In this study, a multicycle disassembly-based decomposition algorithm (MCD-DA) is proposed to efficiently solve the training problem of multiclass support vector machine (SVM). MCD-DA constructs a graph model to re-express the constraints in multiclass SVM, partitions the complex feasible region into simple sub-feasible regions, and designs multiple cycle-based disassembly strategies to update the working variables analytically. Experimental results demonstrate that MCD-DA outperforms typical optimization algorithms for more sample cases.
PATTERN RECOGNITION
(2023)
Article
Automation & Control Systems
Kirandeep Kour, Peter Benner, Sergey Dolgov, Martin Stoll
Summary: This paper proposes a Tensor Train Multi-way Multi-level Kernel (TT-MMK) method, which combines different techniques to achieve more reliable and accurate performance in high-dimensional data classification.
JOURNAL OF MACHINE LEARNING RESEARCH
(2023)
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Biochemical Research Methods
Chaolu Meng, Ying Ju, Hua Shi
Summary: This study used machine learning to explore the mechanism and important components of protein thermostability, and provided an accessible web server.
ANALYTICAL BIOCHEMISTRY
(2022)
Article
Computer Science, Information Systems
Guoquan Li, Linxi Yang, Zhiyou Wu, Changzhi Wu
Summary: Proximal support vector machine (PSVM) is a variant of support vector machine (SVM) which aims to generate a pair of non-parallel hyperplanes for classification. Introducing l(0)-norm regularization in PSVM enables simultaneous selection of important features and removal of redundant features for classification. The proposed method utilizes a continuous nonconvex function and difference of convex functions algorithms (DCA) to solve the optimization problem efficiently.
INFORMATION SCIENCES
(2021)
Article
Environmental Sciences
Guangxin Liu, Liguo Wang, Danfeng Liu, Lei Fei, Jinghui Yang
Summary: This article proposes a non-parallel SVM model, which improves the classification effect and generalization performance for hyperspectral images by adding an additional empirical risk minimization term and bias constraint.
Article
Biochemistry & Molecular Biology
Kang Zhu, Marcin J. Suskiewicz, Chatrin Chatrin, Oyvind Stromland, Bryan W. Dorsey, Vincent Aucagne, Dragana Ahel, Ivan Ahel
Summary: This study reveals ADPr ubiquitylation as a general function of the DELTEX family E3 ligases and presents the evidence of reversible ubiquitylation of ADP-ribosylated nucleic acids.
NUCLEIC ACIDS RESEARCH
(2023)
Review
Computer Science, Information Systems
Arijit Chakraborty, Sajal Mitra, Debashis De, Anindya Jyoti Pal, Ferial Ghaemi, Ali Ahmadian, Massimiliano Ferrara
Summary: Protein-Protein Interaction (PPI) is a crucial network in biology that requires fast, accurate, and critical analysis, with Support Vector Machine (SVM) being an effective tool for solving complex classification problems.
Article
Computer Science, Artificial Intelligence
Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang
Summary: The improved nonparallel support vector machine (INPSVM) proposed in this article inherits the advantages of nonparallel support vector machine (NPSVM) while also offering incomparable benefits over twin support vector machine (TSVM). INPSVM effectively eliminates noise effects and achieves higher classification accuracy for both linear and nonlinear datasets compared to other algorithms. Experimental results demonstrate the superior efficiency, accuracy, and robustness of INPSVM.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Wenchao Miao, Qi Xu, K. H. Lam, Philip W. T. Pong, H. Vincent Poor
Summary: Protection devices are crucial in DC systems, but series arc faults may not be detected by conventional devices, leading to malfunctions and fire hazards. This paper proposes a series arc-fault detection system based on modified EMD technique and SVM algorithm for reliable and efficient operation of DC systems. The effectiveness of arc-fault detection is significantly improved by acquiring accurate arc signatures without predefining various thresholds.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Bagesh Kumar, Ayush Sinha, Sourin Chakrabarti, O. P. Vyas
Summary: In this paper, a fast training method for OCSSVM is proposed, which enhances its scalability without compromising precision significantly. Experimental results show that the proposed method achieves the best tradeoff between training time and accuracy, providing similar accuracies to regular OCSSVM and better scalability compared to existing state-of-the-art one-class classifiers.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Marine
Wenhe Shen, Jianxi Yao, Xinjue Hu, Jialun Liu, Shijie Li
Summary: With the rapid development of Maritime Autonomous Surface Ships (MASS), building an accurate ship dynamics model using system identification method has become a critical issue. This study proposes a non-parametric and robust two-phase system identification method, which filters the data using an improved complete ensemble empirical mode decomposition and models the ship dynamics using Semblance least square support vector machine (S-LS-SVM) with a state-of-the-art Semblance kernel function. Compared to traditional methods, this method significantly improves the Root Mean Square Error (RMSE) of overall prediction on the test dataset.
Article
Computer Science, Information Systems
Jie Sun, Hamido Fujita, Yujiao Zheng, Wenguo Ai
Summary: This paper focuses on multiclass financial distress prediction using SVM and decomposition fusion methods, showing that OVO-SVM outperforms OVR-SVM and ECOC-SVM in overall performance and is preferred. Data preprocessing mechanisms can greatly enhance the model performance, while OVO-SVM is more competitive for predicting financial pseudosoundness and moderate financial distress compared to human expertise.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Bikram Kumar, Deepak Gupta
Summary: The paper introduces a novel method ULTBSVM which utilizes Universum data to enhance the classification of healthy and seizure EEG signals, showing promising results in experiments.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)