Article
Computer Science, Information Systems
Shanlin Zhou, Yan Gu, Hualong Yu, Xibei Yang, Shang Gao
Summary: This article introduces a novel strategy called RUE for estimating location information and cost assignment to address the problem of class-imbalance learning. The strategy indirectly explores location information through a random undersampling ensemble, is robust towards data distribution, and accurately estimates the significance of each instance.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
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
Computer Science, Artificial Intelligence
Anuran Chakraborty, Kushal Kanti Ghosh, Rajonya De, Erik Cuevas, Ram Sarkar
Summary: Class imbalance is a prevalent issue in various domains, where traditional supervised machine learning algorithms often fall short. This paper introduces an undersampling approach based on Particle Swarm Optimization, showing promising performance on imbalanced datasets.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Barenya Bikash Hazarika, Deepak Gupta
Summary: This paper introduces a new support vector machine (SVM) model and an improved least squares SVM model to address class imbalance learning (CIL) in binary classification problems. The algorithms assign weights to samples based on their class distributions during training to reduce the effects of CIL.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Salim Rezvani, Xizhao Wang
Summary: The study introduces a class imbalance learning method using Fuzzy ART and IFTSVM, which effectively addresses classification issues related to class imbalance, noise, outliers, and large-scale datasets.
INFORMATION SCIENCES
(2021)
Article
Biology
Song Yang, Lejing Lou, Wangjia Wang, Jie Li, Xiao Jin, Shijia Wang, Jihao Cai, Fangjun Kuang, Lei Liu, Myriam Hadjouni, Hela Elmannai, Chang Cai
Summary: This paper proposes a new algorithm called SCACO, which combines slime mould foraging behavior and collaborative hunting to improve the convergence accuracy and solution quality of ACOR. It also optimizes the ability of ACO to jump out of local optima using an adaptive collaborative hunting strategy. The performance of SCACO is compared with nine basic algorithms and nine variants, demonstrating its effectiveness in classification prediction for the diagnosis of tuberculous pleural effusion.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Ashima Kukkar, Yugal Kumar, Ashutosh Sharma, Jasminder Kaur Sandhu
Summary: In this study, an ant colony optimization (ACO) based feature extraction technique is proposed for bug severity classification. By integrating the ACO technique with NB, SVM, DeepFM, and F-SVM techniques, bug severity can be predicted and bugs can be classified into multiple severity classes. Experimental evaluations show that these techniques achieve good results in terms of accuracy, precision, recall, and F1-measure.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
M. A. Ganaie, M. Tanveer, Alzheimer's Disease Neuroimaging Initiative
Summary: This paper introduces a novel fuzzy least squares projection twin support vector machines for class imbalance learning, which outperforms baseline models in experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Chemistry, Multidisciplinary
Evangelos Tsagalidis, Georgios Evangelidis
Summary: This article addresses the problem of class imbalance in data mining and machine learning and proposes sampling strategies to tackle the issue. The study reveals that sampling strategies utilizing domain expertise can improve classifier performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Da Hoon Seol, Jeong Eun Choi, Chan Young Kim, Sang Jeen Hong
Summary: Plasma-based semiconductor processing is highly sensitive, and even minor changes in the procedure can have serious consequences. Class imbalance in semiconductor process data poses a significant obstacle to fault detection and classification (FDC) in semiconductor equipment. This study suggests a suitable preprocessing method to address the issue of class imbalance and proposes an effective sampling strategy using the SMOTE-TOMEK model to improve FDC performance in plasma-based semiconductor process data.
Article
Computer Science, Artificial Intelligence
Barenya Bikash Hazarika, Deepak Gupta, Parashjyoti Borah
Summary: A new fuzzy twin support vector machine based on affinity and class probability (ACFTSVM) is proposed to enhance the classification performance of the affinity and class probability-based fuzzy support vector machine (ACFSVM). ACFTSVM adds regularization terms to diminish the negative influence of noise, measures the affinity and estimates the class probability of majority class datapoints to decrease the potential of noises. ACFTSVM gives preference to majority class datapoints with higher affinity and class probability, while minimizing the influence of minority class samples with lower affinity and class probability.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
M. A. Ganaie, M. Tanveer, Alzheimer's Disease Neuroimaging Initiative
Summary: In real world problems, the imbalance of data samples presents a challenge for classification models. This paper proposes a K-nearest neighbor based weighted reduced universum twin support vector machine model to address class imbalance issues by incorporating local neighborhood information and utilizing universum data, resulting in improved generalization performance.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xinsen Zhou, Wenyong Gui, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen
Summary: Continuous ant colony optimization algorithm incorporates a random following strategy to enhance global optimization performance and effectively handle high-dimensional feature selection problems. The algorithm performs competitively with other state-of-the-art algorithms in benchmark tests and outperforms well-known classification methods on high-dimensional datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Theory & Methods
Joshua Peake, Martyn Amos, Nicholas Costen, Giovanni Masala, Huw Lloyd
Summary: This paper presents an improved algorithm for the Virtual Machine Placement (VMP) problem, which significantly improves the solution speed by utilizing parallelization techniques and modern processor technologies. The algorithm achieves solution qualities comparable to or even superior to other nature-inspired methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Biotechnology & Applied Microbiology
Ke-Fan Wang, Jing An, Zhen Wei, Can Cui, Xiang-Hua Ma, Chao Ma, Han-Qiu Bao
Summary: In this paper, a novel imbalance classification method based on deep learning and fuzzy support vector machine, named DFSVM, is proposed. The method utilizes a deep neural network to obtain an embedding representation of the data and performs oversampling in the embedding space to address the data imbalance issue. Furthermore, a fuzzy support vector machine is used as the final classifier to improve the classification quality of minority classes.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Hong Shi, Pingxin Wang, Xin Yang, Hualong Yu
Summary: This study proposes an improved clustering algorithm for handling incomplete data sets caused by random noise, data loss, data acquisition limitations, etc. The algorithm divides the data set into two sets based on missing and non-missing values, and uses mean imputation to fill the missing attribute values. Experimental results demonstrate the effectiveness of the algorithm in clustering.
NEURAL PROCESSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Shang Gao, Wenlu Dong, Ke Cheng, Xibei Yang, Shang Zheng, Hualong Yu
NEURAL PROCESSING LETTERS
(2020)
Article
Computer Science, Software Engineering
Shang Zheng, Jinjing Gai, Hualong Yu, Haitao Zou, Shang Gao
SCIENTIFIC PROGRAMMING
(2020)
Article
Computer Science, Software Engineering
Ruihan Cheng, Longfei Zhang, Shiqi Wu, Sen Xu, Shang Gao, Hualong Yu
Summary: In this paper, a novel solution of Class Imbalance Learning called Probability Density Machine (PDM) is introduced, which is an improved Gaussian Naive Bayes (GNB) model and shows promising results in experiments.
SCIENTIFIC PROGRAMMING
(2021)
Article
Chemistry, Analytical
Yadong Cai, Shiqi Wu, Ming Zhou, Shang Gao, Hualong Yu
Summary: Gas explosion is a significant factor impacting coal mine safety, and employing the probability density machine algorithm can effectively prevent gas explosion accidents and enhance early warning accuracy.
Article
Mathematics
Wangwang Yan, Jing Ba, Taihua Xu, Hualong Yu, Jinlong Shi, Bin Han
Summary: This study proposes a novel attribute selector called Beam-Influenced Selector (BIS), which enhances the stability of attribute reduction through random partition and beam strategies. Experimental results show that this selector significantly improves the stability of the derived reducts and achieves excellent performance in classification tasks.
Article
Computer Science, Artificial Intelligence
Aimin Zhang, Hualong Yu, Shanlin Zhou, Zhangjun Huan, Xibei Yang
Summary: This study presents the instance weighted SMOTE (IW-SMOTE) algorithm, which improves the SMOTE algorithm by indirectly exploiting distribution data. It uses an UnderBagging-like undersampling ensemble algorithm to classify each training instance and acquire confusing information. Based on the confusing information, the algorithm accurately estimates the location information of each instance and handles noisy and borderline instances accordingly. The balanced instance set is then used to train multiple classifiers to verify the algorithm's generality and effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yan Gu, Hualong Yu, Xibei Yang, Shang Gao
Summary: This paper introduces an improved active learning algorithm AL-SNN-ELM, which combines shared nearest neighbor clustering algorithm and online-sequential extreme learning machine. It can reduce labeling cost and improve performance simultaneously.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Information Systems
Xing Zong, Guiyu Li, Shang Zheng, Haitao Zou, Hualong Yu, Shang Gao
Summary: Heterogeneous Cross-Project Defect Prediction (HCPDP) aims to learn a prediction model from a heterogeneous source project and apply it to a target project. This paper introduces optimal transport (OT) theory to establish the relationship between source and target data distributions and proposes two prediction algorithms based on OT theory. Experimental results demonstrate the effectiveness of the proposed methods in helping developers identify defects in the early phase of software development.
Article
Computer Science, Information Systems
Shanlin Zhou, Yan Gu, Hualong Yu, Xibei Yang, Shang Gao
Summary: This article introduces a novel strategy called RUE for estimating location information and cost assignment to address the problem of class-imbalance learning. The strategy indirectly explores location information through a random undersampling ensemble, is robust towards data distribution, and accurately estimates the significance of each instance.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Yan Gu, Jicong Duan, Hualong Yu, Xibei Yang, Shang Gao
Summary: This study focuses on the challenge of designing an effective query strategy for measuring uniform information about unlabeled instances in multi-label active learning (MLAL). The proposed query strategy, PLVI-CE, combines uncertainty and diversity measures to reduce labeling cost and obtain an accurate and robust classification model. Additionally, the study explores the use of label-weighted extreme learning machine (LW-ELM) as the base classifier, which has advantages such as low computational cost and strong generalization performance, and can handle multi-label data with class imbalance distributions.
APPLIED INTELLIGENCE
(2023)
Article
Mathematics
Jinghong Zhang, Yingying Li, Bowen Liu, Hao Chen, Jie Zhou, Hualong Yu, Bin Qin
Summary: With the expansion of data scale and diversity, the issue of class imbalance has become increasingly salient. To address these challenges, a novel fuzzy classifier is proposed that can handle classification tasks with class-imbalanced data.
Article
Computer Science, Information Systems
Fuchen Kong, Qi Wang, Shang Gao, Hualong Yu
Summary: This paper proposes an optimized DQN algorithm called B-APFDQN for UAV path planning, which combines Artificial Potential Field (APF) as prior knowledge and uses a multi-output neural network. It also introduces a SA-$\varepsilon$-greedy algorithm to automatically adjust the exploration frequency and prevent falling into local optima. With the removal of unnecessary nodes and the use of the B-spline algorithm, the obtained paths are smoother and shorter. Simulation experiments show that the proposed B-APFDQN algorithm outperforms classical DQN and is effective in UAV path planning.
Article
Computer Science, Artificial Intelligence
Jicong Duan, Yan Gu, Hualong Yu, Xibei Yang, Shang Gao
Summary: Multi-label learning has a wide range of real-world applications and the problem of class imbalance in multilabel data has been less addressed. This study proposes the ECC++ algorithm family, which combines the ensemble classifier chain algorithm with binary-class imbalance learning techniques to tackle the challenges of class imbalance and label correlations. Experimental results demonstrate the effectiveness and superiority of ECC++ over existing class imbalance multi-label learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Information Systems
Baoquan Feng, Yan Gu, Hualong Yu, Xibei Yang, Shang Gao
Summary: This study proposes a novel incremental learning algorithm called distribution matching ensemble (DME) for adaptive weighted ensemble learning. DME estimates the distribution of each data block and maintains a group of classifiers in a buffer. When a new data block is received, the similarity between its distribution and reserved distributions is calculated to guide weight assignment for adaptive ensemble decision. Experiments show that DME can track and adapt to various types of concept drift, outperforming state-of-the-art algorithms.
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.