Article
Computer Science, Artificial Intelligence
Keyu Liu, Tianrui Li, Xibei Yang, Xin Yang, Dun Liu
Summary: This paper introduces a novel ensemble feature selection method, which selects features with local significance through cross-class sample granulation and ensemble feature selection strategies.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Shangzhi Wu, Litai Wang, Shuyue Ge, Zhengwei Hao, Yulin Liu
Summary: This paper proposes a neighborhood rough set model based on neighborhood equivalence relation (NMER) and designs a corresponding feature selection algorithm. Experimental results demonstrate the effectiveness of the algorithm in selecting main and useful features.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shuangjie Li, Kaixiang Zhang, Yali Li, Shuqin Wang, Shaoqiang Zhang
Summary: Feature selection is crucial in many fields, especially in machine learning. The proposed method OFS-Gapknn effectively addresses the challenges of online streaming features by defining a new neighborhood rough set relation and analyzing relevance and redundancy features. Experimental results demonstrate the dominance and significance of this method.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Cheng Wang, Guoyin Wang, Xinbo Gao, Weiping Ding, Jianhang Yu, Yujia Zhai, Zizhong Chen
Summary: This article introduces a granular-ball rough set (GBRS) model based on granular-ball computing, which can process continuous data and use equivalence classes for knowledge representation. Experimental results demonstrate that GBRS outperforms traditional rough set models in terms of learning accuracy and feature selection.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Zhong Yuan, Tianrui Li, Xiaoling Yang, BinBin Sang
Summary: A novel feature selection method considering feature interaction is proposed in this study, with an algorithm called NCMI_IFS developed. Experimental results demonstrate that the algorithm exhibits higher classification performance and significant effectiveness on multiple public datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Swarnajyoti Patra, Barnali Barman
Summary: A novel feature selection technique based on rough set theory is proposed in this work to reduce the dimensionality of hyperspectral images. The technique defines a new criterion by combining relevance and significance measures, and adopts a first order incremental search to select the most informative bands, showing better results compared to existing techniques. The proposed dependency measure definition is completely parameter free and computationally very cheap.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jinghua Liu, Yaojin Lin, Weiping Ding, Hongbo Zhang, Cheng Wang, Jixiang Du
Summary: In this paper, a novel multi-label feature selection method based on label distribution and neighborhood rough set (LDRS) is proposed. The method captures the significance of labels and evaluates the quality of features, while considering label-specific features. Experimental results demonstrate the advantages of the proposed method.
Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Jinghua Liu, Yaojin Lin, Jixiang Du, Hongbo Zhang, Ziyi Chen, Jia Zhang
Summary: This paper proposes a novel online streaming feature selection method for multi-label learning based on neighborhood rough set model, taking into account feature significance, feature redundancy, and label space integrity. Experimental results demonstrate the advantages of the proposed method.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoling Yang, Hongmei Chen, Tianrui Li, Jihong Wan, Binbin Sang
Summary: This paper introduces a novel neighborhood rough set Model based on Distance metric learning (NMD) to improve the discriminative ability and reduce uncertainty in representation. Experimental results demonstrate the effectiveness and superiority of the proposed feature selection algorithms on real-world datasets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Wenjing Wang, Min Guo, Tongtong Han, Shiyong Ning
Summary: Feature selection is a valuable strategy in data mining, pattern recognition, and machine learning. However, most existing methods do not consider feature interaction when calculating correlations. This study proposes a novel feature selection algorithm that takes into account feature relevance, redundancy, and interaction. The algorithm introduces a new information measurement method called neighborhood symmetric uncertainty and develops an objective evaluation function for interactive selection. The results show that the proposed algorithm effectively reduces dimensionality and achieves the best average classification accuracy among the compared algorithms.
INTELLIGENT DATA ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Jiucheng Xu, Meng Yuan, Yuanyuan Ma
Summary: Feature selection based on the fuzzy neighborhood rough set model (FNRS) is popular in data mining, but it may lead to the loss of information due to the dependency function only considering the lower approximation of the decision. This paper proposes a fuzzy neighborhood joint entropy model (FNSIJE) to address this problem, introducing uncertain fuzzy neighborhood self-information measures of decision variables and an uncertainty measure based on fuzzy neighborhood joint entropy for feature selection. The model shows better classification performance and can reduce dimensionality effectively.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Javad Hamidzadeh, Ebrahim Rezaeenik, Mona Moradi
Summary: This paper proposes a new one-class classifier to predict ratings in recommendation systems and reduces the impact of noise on results by estimating shared informative neighbors of each user using a probability fuzzy rough set method and a quarter-sphere SVM classifier. The proposed method outperforms other six methods in terms of accuracy, recall, precision, and computational time, as confirmed through extensive experiments on real-world data sets.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Yanyan Yang, Degang Chen, Xiao Zhang, Zhenyan Ji, Yingjun Zhang
Summary: Incremental feature selection is an efficient method that updates the optimal feature subset without forgetting previous knowledge. This study proposes a novel approach using sample selection and feature-based accelerator, which avoids redundant calculations and demonstrates time efficiency.
APPLIED SOFT COMPUTING
(2022)
Article
Environmental Sciences
Yue Pan, Yan Chen, Yue Liu
Summary: With increasing human activities, the vulnerability of water, energy, food, and ecological systems has become more prominent. This study evaluates the vulnerability of the water-energy-food-ecology (WEFE) nexus in the Yangtze River Economic Belt (YREB), predicts future vulnerability, and provides insights for policy-making to reduce vulnerability.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
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.