Article
Computer Science, Artificial Intelligence
Xijiong Xie, Yujie Xiong
Summary: This paper proposes two generalized multi-view extensions of generalized eigenvalue proximal support vector machines, which utilize multi-view co-regularization term and weighted value to mine consistency and complementarity information. Experimental results demonstrate that these methods outperform relevant two-view classification algorithms in terms of performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Multidisciplinary Sciences
Yuanyuan Chen, Zhixia Yang
Summary: Functional data analysis is a research hotspot in data mining, with traditional methods treating functional data as discrete observations. This paper introduces a functional generalized eigenvalue proximal support vector machine (FGEPSVM) that finds two nonparallel hyperplanes in function space for classification. By using higher-order derivatives and introducing orthonormal basis, the problem in function space is transformed into vector space for improved classification accuracy.
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
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)
Article
Computer Science, Information Systems
M. Tanveer, Tarun Gupta, Miten Shah
Summary: This article introduces a new clustering algorithm pinTSVC to address the issues of noise sensitivity and re-sampling instability, by incorporating the pinball loss function for enhanced stability and performance in noise-corrupted datasets.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Umesh Gupta, Deepak Gupta
Summary: This paper presents two efficient variant models to handle noise and outliers, obtaining solutions by solving a system of linear equations and minimizing the impact of noise. The proposed models demonstrate exceptional generalization performance.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaohan Zheng, Li Zhang, Leilei Yan
Summary: This paper proposes a novel sparse discriminant twin support vector machine (SD-TSVM) to improve the discriminant ability and sparsity compared to traditional TSVM. The introduction of twin Fisher regularization terms and the utilization of 1-norm of coefficients and hinge loss contribute to its satisfactory performance.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hossein Moosaei, M. A. Ganaie, Milan Hladik, M. Tanveer
Summary: Imbalanced datasets are common in real-world problems. Traditional classification algorithms have limitations in handling imbalanced data. To improve classification performance on imbalanced datasets, an improved reduced universum twin support vector machine (IRUTSVM) algorithm is proposed, which introduces new constraints and reduces computational time.
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)
Article
Computer Science, Information Systems
Bo Liu, Ruiguang Huang, Yanshan Xiao, Junrui Liu, Kai Wang, Liangjiao Li, Qihang Chen
Summary: This paper introduces the role of Universum in supervised and semi-supervised learning and incorporates it into TWSVM to improve generalization performance. To enhance generalization performance in complex environments, an adaptive robust Adaboost-based twin support vector machine with universum learning (ARABUTWSVM) is proposed.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Huajuan Huang, Xiuxi Wei, Yongquan Zhou
Summary: This article reviews the recent developments in twin support vector regression (TSVR). It introduces the basic concepts and models of TSVR, summarizes the improved algorithms and applications in recent years, and analyzes the advantages and disadvantages of representative algorithms through experiments. The article also discusses the research conducted on TSVR.
Article
Operations Research & Management Science
M. A. Ganaie, M. Tanveer, Jatin Jangir
Summary: In this study, a novel universum twin support vector machine with pinball loss function (Pin-UTSVM) is proposed for the classification of EEG signals. The Pin-UTSVM model is more robust to noise compared to existing models and performs better in experimental results.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Abolfazl Hasanzadeh Shadiani, Mahdi Aliyari Shoorehdeli
Summary: This paper proposes an online approach for twin support vector machine, which utilizes recursive relation to avoid repetitive calculation of inverse matrices, resulting in improved training efficiency and maintained accuracy. Experimental results demonstrate the effectiveness of this method.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Ganesan Kalaiarasi, Sureshbabu Maheswari
Summary: In this study, an effective classification of hyperspectral images was modeled and simulated with the proximal support vector machine (PSVM) by integrating them with the deep learning approach. The new deep PSVM classifiers, designed to handle the complexity, discrepancies, and irregularities in traditional hyperspectral image classifiers, showed better classification accuracy compared to other techniques.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Huiru Wang, Jiayi Zhu, Feng Feng
Summary: In this paper, a new classifier called elastic net twin support vector machine (ETSVM) is introduced to enhance classification performance. ETSVM resolves two smaller-sized quadratic programming problems (QPPs) similar to twin support vector machine (TSVM), but with the use of elastic net penalty for slack variables. The key difference is that ETSVM does not involve matrix inversion, avoiding ill-conditioning cases. Theoretical properties are discussed and safe screening rules (SSR-ETSVM) are derived to increase computing efficiency. Comparison with other methods confirms the rationality and effectiveness of the proposed algorithms.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yuting Sun, Shifei Ding, ZiChen Zhang, Chenglong Zhang
Summary: A semi-supervised support vector machine algorithm based on hypergraph was proposed in this study to effectively utilize unlabeled data for semi-supervised learning, introducing manifold regularization term to capture the complex relations between data. Experiments demonstrated the effectiveness of the algorithm in binary and multi-category classification tasks.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Nan Zhang, Shiliang Sun
Summary: Multiview clustering is an important research topic, and incomplete views of data instances are common in real-world scenarios. To address this issue, we propose an effective incomplete multiview nonnegative representation learning framework that can handle incomplete multiview clustering in various situations and achieves better results compared to other state-of-the-art algorithms.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Jian Zhang, Shifei Ding, Tongfeng Sun, Lili Guo
Summary: The paper proposes a Gaussian Restricted Boltzmann Machine with binary Auxiliary units (GARBM) for image processing, which can extract real-valued features and alleviate the overfitting problem. By designing binary auxiliary units in the visible layer and constructing parameterized real-valued features in the hidden layer, the proposed GARBM-based deep neural networks achieve effective image recognition and generation tasks.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Automation & Control Systems
Nan Zhang, Shiliang Sun
Summary: The study introduces a novel multiview graph RBM model that simultaneously conducts local structural learning and multiview representation learning, achieving superior performance in multiview classification tasks compared to other state-of-the-art multiview classification algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Shifei Ding, Wei Du, Chao Li, Xiao Xu, Lijuan Wang, Ling Ding
Summary: The improved density peaks clustering algorithms (DPCV and MDDPC) based on the density peaks clustering algorithm have improved clustering accuracy by using methods such as variance and Manhattan distance, while solving problems such as the inability to effectively identify cluster centers and the chain reaction caused by error allocation.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Shifei Ding, Yuting Sun, Jian Zhang, Lili Guo, Xiao Xu, Zichen Zhang
Summary: Semi-supervised learning is widely used in machine learning, but most methods are not suitable for regression. This paper proposes a hypergraph regularized support vector regression (HGSVR) method which utilizes the hypergraph to represent the geometric structure of data. Additionally, a two-layer maximum density minimum redundancy method (MDMR) is introduced to pre-select initial labeled data, and a second semi-supervised regression called MDMR-HGSVR is proposed. Experimental results on 9 UCI datasets demonstrate the superiority of HGSVR and MDMR-HGSVR over other compared methods.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Lili Guo, Yanru Wang, Fanchao Wang, Ling Ding, Shifei Ding
Summary: This article proposes a new image super-resolution reconstruction method called P-WAN, which can improve the utilization of image features from different layers and overcome the problem of blurred texture edges in reconstructed images due to lack of high frequency information. Based on P-WAN, a DP-WAN method is further proposed, which reconstructs better high frequency information and higher quality texture edges by adding discrete wavelet transform. Experimental results demonstrate that the proposed method achieves the best performance in objective evaluation and provides a good visual experience in subjective visual evaluation.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Shifei Ding, Chenglong Zhang, Jian Zhang, Lili Guo, Ling Ding
Summary: Broad learning system (BLS) is a faster modeling framework. However, the incremental mode lacks self-supervision mechanism, which limits its adaptability. In this study, a novel incremental multilayer BLS based on the stochastic configuration (SC) algorithm is proposed for regression, named IMLBLS-SC. Experimental results show that IMLBLS-SC outperforms other models.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wei Du, Shifei Ding, Chenglong Zhang, Zhongzhi Shi
Summary: Most recent research on MARL has focused on deploying cooperative policies for homogeneous agents, but realistic multiagent environments may have heterogeneous agents. To tackle the challenges posed by heterogeneity and diverse relationships, the researchers propose a novel method that uses a heterogeneous graph attention network to model the relationships between heterogeneous agents.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lili Guo, Shifei Ding, Longbiao Wang, Jianwu Dang
Summary: This article introduces a deep spectro-temporal-channel network (DSTCNet) for speech emotion recognition, which improves the representation ability by integrating multiple spectro-temporal-channel attention modules. Experimental results show that DSTCNet outperforms traditional CNN-based methods and several state-of-the-art methods in emotion recognition.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Nan Zhang, Shiliang Sun
Summary: Multivariate time series clustering is a significant research topic in time series learning, aiming to discover correlations among multiple sequences and divide multimodal time series data into subsets. This paper proposes a novel unsupervised shapelet learning with adaptive neighbors (USLA) model for learning salient multivariate subsequences. The paper also introduces a multiview USLA (MUSLA) model which treats different-length shapelets as different views, achieving better performance on real-world multivariate time series datasets.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Shifei Ding, Wei Du, Ling Ding, Jian Zhang, Lili Guo, Bo An
Summary: Communication learning is an important research direction in the MARL domain. GNNs can aggregate neighbor nodes' information for representation learning. However, simply aggregating the information of neighboring agents through GNNs may not extract enough useful information, and the topological relationship information is ignored.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shifei Ding, Chao Li, Xiao Xu, Lili Guo, Ling Ding, Xindong Wu
Summary: This paper proposes a horizontal federated DPC (HFDPC) algorithm, which introduces the idea of horizontal federated learning and uses similar density chain (SDC) to address the issues of privacy data leakage and the Domino effect in DPC algorithm. Experimental results show improvements in accuracy and speed.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shiliang Sun, Nan Zhang
Summary: This paper introduces a novel Incomplete Multiview Nonnegative representation learning model (IMNGA) that simultaneously performs graph learning, missing graph completion, and consensus representation learning, effectively addressing the issue of modeling correlations in incomplete multiview clustering.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Lili Guo, Longbiao Wang, Jianwu Dang, Yahui Fu, Jiaxing Liu, Shifei Ding
Summary: This article proposes an implicitly aligned multimodal transformer fusion framework based on acoustic features and text information for emotion recognition. The model allows two modalities to guide and complement each other, and uses weighted fusion to control the contributions of different modalities, thereby obtaining more complementary emotional representations. Experiments have shown that this method outperforms baseline methods.
