Article
Computer Science, Information Systems
Xiaoling Yang, Hongmei Chen, Tianrui Li, Pengfei Zhang, Chuan Luo
Summary: This paper investigates the feature selection problem in fuzzy rough set theory and proposes a new feature selection model based on Student-t kernel and fuzzy divergence. Experimental results demonstrate the effectiveness of the proposed model on real-world datasets.
INFORMATION SCIENCES
(2022)
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
Shuang An, Jiaying Liu, Changzhong Wang, Suyun Zhao
Summary: This study proposes a relative uncertainty measure by combining relative measure with the lower approximation of FRSs, which addresses the problem of FRS theory's inefficiency in evaluating classification uncertainty with large class density differences. The designed fuzzy rough feature selection algorithm demonstrates good performance, indirectly proving the effectiveness and efficiency of the relative uncertainty measure in classification tasks.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2021)
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Tianrui Li, Binbin Sang, Zhong Yuan
Summary: This study proposes a method for feature selection in data with uncertainty, fuzziness, and noise. A robust fuzzy rough set model is constructed to enhance the robustness and antinoise ability. Uncertainty measures are defined to analyze the interactivity and redundancy of features. Experimental results demonstrate the significance of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Binbin Sang, Lei Yang, Hongmei Chen, Weihua Xu, Xiaoyan Zhang
Summary: This paper proposes a robust fuzzy dominance rough set model to tackle noise interference and develops a feature selection method based on this model for ordinal classification tasks. The robust model is constructed using a non-linear vague quantifier and related dependency function, while the contribution of feature combinations for ordinal classification is characterized using rank entropy-based uncertainty measures. A new feature evaluation index and corresponding feature selection algorithm are also presented. Numerical experiments demonstrate the effectiveness of the proposed model and feature selection method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Pankhuri Jain, Anoop Kumar Tiwari, Tanmoy Som
Summary: This study introduces a novel approach to enhance the prediction of anti-tubercular peptides by extracting sequence features, selecting optimal features, and utilizing different machine learning techniques. The proposed method outperforms previous methods and achieves high accuracy and sensitivity rates.
Article
Computer Science, Artificial Intelligence
Jinkun Chen, Yaojin Lin, Jusheng Mi, Shaozi Li, Weiping Ding
Summary: Feature evaluation is important in constructing a feature selection algorithm in kernelized fuzzy rough sets. This article studies the problem of feature selection using spectral graph theory. It introduces operators to capture sample affinity and presents a feature evaluation function and its corresponding algorithm. Experimental results demonstrate the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Likui An, Sinan Ji, Changzhong Wang, Xiaodong Fan
Summary: A novel model, weighted multigranulation fuzzy decision rough sets, is proposed in this paper, which uses Gaussian kernel to compute similarity and obtain multiple fuzzy granulations from multisource fuzzy information system. The proposed method is compared with Sun's multigranulation rough set model, demonstrating its effectiveness in multisource data analysis.
Article
Computer Science, Information Systems
Zhihong Wang, Hongmei Chen, Xiaoling Yang, Jihong Wan, Tianrui Li, Chuan Luo
Summary: Dimensionality reduction is an important step in many learning methods to achieve optimal performance using discriminative features. This study proposes a fuzzy rough dimensionality reduction method that combines feature selection and feature extraction, and compares its performance with other algorithms, showing higher classification performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoling Yang, Hongmei Chen, Hao Wang, Tianrui Li, Zeng Yu, Zhihong Wang, Chuan Luo
Summary: This article proposes a local density-based fuzzy rough set (LDFRS) model to handle noisy data, and introduces mutual information to evaluate uncertainty in data. Furthermore, a joint feature evaluation function on the indispensability and relevance of features is constructed. Based on these works, a fuzzy rough feature selection algorithm is developed, and experimental results demonstrate the robustness and effectiveness of the proposed model.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jihong Wan, Hongmei Chen, Tianrui Li, Xiaoling Yang, Binbin Sang
Summary: Feature selection is a crucial data preprocessing approach in data mining, and the interaction between features and their dynamic changes should be taken into consideration to prevent the loss of useful information.
INFORMATION SCIENCES
(2021)
Article
Operations Research & Management Science
Shivam Shreevastava, Priti Maratha, Tanmoy Som, Anoop Kumar Tiwari
Summary: This article proposes an innovative framework called intuitionistic fuzzy rough set (IFRS) to deal with uncertainty in judgement and identification. It integrates IF set and rough set with the concept of (alpha, beta)-indiscernibility to avoid noise. The framework is supported with proofs and a feature selection method is presented using this framework. A concrete illustration and comparative study with other methods are provided to demonstrate the effectiveness and superiority of the proposed technique.
Article
Computer Science, Artificial Intelligence
Pankhuri Jain, Anoop Tiwari, Tanmoy Som
Summary: This paper introduces a technique for missing value imputation and feature selection using fuzzy rough set-based approaches. The experimental results demonstrate its high applicability and robustness, as well as its ability to significantly reduce data dimensionality while maintaining high performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Pei Liang, Dingfei Lei, KwaiSang Chin, Junhua Hu
Summary: The current research on fuzzy rough sets for feature selection faces two major problems: the difficulty in evaluating the importance of feature subsets accurately in high-dimensional data space due to the use of multiple intersection operations of fuzzy relations, and the sensitivity to noisy information in the classical fuzzy rough sets model. To address these issues, this study proposes a radial basis function kernel-based similarity measure and introduces a relative classification uncertainty measure to improve the robustness of the fuzzy rough sets model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ramesh Kumar Huda, Haider Banka
Summary: Feature selection is the process of selecting criterion functions and search strategies to find the best feature subset from a large number of subsets. Algorithms based on particle swarm optimization and fuzzy rough fitness function can effectively select optimal feature subsets from datasets with numerous features.
Article
Computer Science, Artificial Intelligence
Xixi Jia, Xiangchu Feng, Weiwei Wang, Lei Zhang
Summary: Spectral regularization is a commonly used approach for low-rank matrix recovery, but existing LRMR solvers are computationally expensive. A generalized unitarily invariant gauge function is proposed for LRMR, offering a bilinear variational problem that can be efficiently solved without SVD computation. The GUIG model is shown to be more accurate and faster than state-of-the-art algorithms, especially for large-scale problems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang
Summary: Image cropping aims to improve the composition and aesthetic quality of an image by removing extraneous content. However, existing image cropping databases and evaluation metrics fail to reflect the non-uniqueness and flexibility of image cropping. This work presents a grid anchor based formulation for image cropping, reduces the searching space of candidate crops, and constructs a more reliable benchmark. In addition, a lightweight cropping model is designed to efficiently produce visually pleasing crops for different scenes.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yingjie Yin, De Xu, Xingang Wang, Lei Zhang
Summary: The proposed DDEAL method for fast VOS does not rely on online fine-tuning and achieves state-of-the-art performance on two datasets with fast speed and minimal accuracy loss in a faster version.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Lida Li, Jiangtao Xie, Peihua Li, Lei Zhang
Summary: This study introduces a novel architecture that effectively utilizes second-order pooling while maintaining model complexity unchanged during inference. During training, auxiliary second-order pooling networks help the backbone first-order network learn more discriminative feature representations. After training, all auxiliary branches can be removed, and only the backbone first-order network is used for inference.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hang Wang, Youtian Du, Yabin Zhang, Shuai Li, Lei Zhang
Summary: This paper proposes a novel one-stage approach called VRR-TAMP, which formulates the task of VRR as an end-to-end bounding box regression problem by effectively integrating Transformers and an adaptive message passing mechanism. Experimental results demonstrate that our approach significantly outperforms its one-stage competitors and achieves competitive results with the state-of-the-art multi-stage methods.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Multidisciplinary Sciences
Teresa Chung, Issan Yee San Tam, Nelly Yan Yan Lam, Yanni Yang, Boyang Liu, Billy He, Wengen Li, Jie Xu, Zhigang Yang, Lei Zhang, Jian Nong Cao, Lok-Ting Lau
Summary: This study proposes an ensemble machine-learning model that can detect adulteration without prior knowledge of specific substances. By using standard industrial testing data as input, the model can monitor and flag suspicious samples, contributing to public food safety.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoming Li, Shiguang Zhang, Shangchen Zhou, Lei Zhang, Wangmeng Zuo
Summary: This paper proposes a blind face restoration method that explicitly memorizes generic and specific features through dual dictionaries to improve the performance of blind face restoration. By learning generic and specific dictionaries and combining the dictionary transform module and multi-scale dictionaries, the restoration results are improved.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jianqi Ma, Shi Guo, Lei Zhang
Summary: This paper proposes a method to improve the resolution and visual quality of scene text images by embedding text recognition prior into the super-resolution model, which also boosts the performance of text recognition. Experimental results show that this method effectively improves the visual quality of scene text images and significantly enhances the text recognition accuracy.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Qi Liu, Hao Qian, Biao Xiang, Qing Cui, Jun Zhou, Enhong Chen
Summary: This paper proposes a novel model called EATN for accurately classifying sentiment polarities towards aspects in multiple domains in sentiment analysis tasks. The model incorporates a Domain Adaptation Module (DAM) to learn common features and uses multiple-kernel selection method to reduce feature discrepancy among domains. Additionally, EATN includes an aspect-oriented multi-head attention mechanism to capture the direct associations between aspects and contextual sentiment words. Extensive experiments on six public datasets demonstrate the effectiveness and universality of the proposed method compared to current state-of-the-art methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Xiaoming Li, Chaofeng Chen, Xianhui Lin, Wangmeng Zuo, Lei Zhang
Summary: Designing proper training pairs is crucial for superresolving real-world low-quality images. Previous methods focus on modeling degradation with limited improvement. This study uses real-world low-quality face images to model complex degradation and transfers it to natural images to synthesize realistic low-quality counterparts. The results show that this method can effectively learn the real degradation process and improve the quality of non-facial areas.
COMPUTER VISION - ECCV 2022, PT XVIII
(2022)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, Radu Timofte
Summary: Recent works have shown that using a denoiser as the image prior can improve the performance of plug-and-play image restoration methods. However, existing methods are limited by the lack of suitable denoiser priors. In this study, we propose a deep denoiser prior that significantly outperforms other state-of-the-art model-based and learning-based methods for various image restoration tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Binghui Chen, Weihong Deng, Biao Wang, Lei Zhang
Summary: This article emphasizes the importance of generalization ability in deep metric learning for zero-shot image retrieval and clustering tasks. It proposes a confusion-based metric learning framework that uses energy confusion and diversity confusion regularization terms to optimize a robust metric. The framework confuses the learned model in an adversarial manner and serves as an efficient regularization for deep metric learning. Experimental results demonstrate the significance of learning an embedding/metric with good generalization.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jun-Yan He, Shi-Hua Liang, Xiao Wu, Bo Zhao, Lei Zhang
Summary: This paper introduces an efficient multi-granularity based semantic segmentation network (MGSeg) for real-time semantic segmentation, which models the relationship between multi-scale geometric details and high-level semantics for fine granularity segmentation. By employing strategies such as a light-weight backbone and Hybrid Attention Feature Aggregation, the proposed method achieves state-of-the-art performance.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Shi Guo, Zhetong Liang, Lei Zhang
Summary: This work studies the joint denoising and demosaicking problem for real-world burst images, proposing a GCP-Net method that uses green channel prior to guide feature extraction and upsampling for reducing noise impact and preserving more image structures and details. Experiments show the effectiveness of GCP-Net quantitatively and qualitatively on synthetic and real-world noisy images.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Minghan Li, Xiangyong Cao, Qian Zhao, Lei Zhang, Deyu Meng
Summary: This paper proposes a novel approach for rain/snow removal from surveillance videos, taking into account the dynamic statistics of rain/snow and background scenes in video sequences. The method encodes rain/snow using an online multi-scale convolutional sparse coding model, and incorporates a transformation operator for capturing background transformations. The proposed model shows high adaptability to dynamic rain/snow and background changes, and is efficient for real-time video processing.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Timotheus Kampik, Kristijonas Cyras, Jose Ruiz Alarcon
Summary: This paper presents a formal approach to explaining changes in inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). The approach traces the causes of strength inconsistencies and provides explanations for them.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Xiangnan Zhou, Longchun Wang, Qingguo Li
Summary: This paper aims to establish a closer connection between domain theory and Formal Concept Analysis (FCA) by introducing the concept of an optimized concept for a formal context. With the utilization of optimized concepts, it is demonstrated that the class of formal contexts directly corresponds to algebraic domains. Additionally, two subclasses of formal contexts are identified to characterize algebraic L-domains and Scott domains. An application is presented to address the open problem of reconstructing bounded complete continuous domains using attribute continuous contexts, and the presentation of algebraic domains is extended to a categorical equivalence.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Sihan Wang, Zhong Yuan, Chuan Luo, Hongmei Chen, Dezhong Peng
Summary: Anomaly detection is widely used in various fields, but most current methods only work for specific data and ignore uncertain information such as fuzziness. This paper proposes an anomaly detection algorithm based on fuzzy rough entropy, which effectively addresses the similarity between high-dimensional objects using distance and correlation measures. The algorithm is compared and analyzed with mainstream anomaly detection algorithms on publicly available datasets, showing superior performance and flexibility.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Mario Alviano, Francesco Bartoli, Marco Botta, Roberto Esposito, Laura Giordano, Daniele Theseider Dupre
Summary: This paper investigates the relationships between a multipreferential semantics in defeasible reasoning and a multilayer neural network model. Weighted knowledge bases are considered for a simple description logic with typicality under a concept-wise multipreference semantics. The semantics is used to interpret MultiLayer Perceptrons (MLPs) preferentially. Model checking and entailment based approach are employed in verifying conditional properties of MLPs.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Bazin Alexandre, Galasso Jessie, Kahn Giacomo
Summary: Formal concept analysis is a mathematical framework that represents the information in binary object-attribute datasets using a lattice of formal concepts. It has been extended to handle more complex data types, such as relational data and n-ary relations. This paper presents a framework for polyadic relational concept analysis, which extends relational concept analysis to handle relational datasets consisting of n-ary relations.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Ander Gray, Marcelo Forets, Christian Schilling, Scott Ferson, Luis Benet
Summary: The presented method combines reachability analysis and probability bounds analysis to handle imprecisely known random variables. It can rigorously compute the temporal evolution of p-boxes and provide interval probabilities for formal verification problems. The method does not impose strict constraints on the input probability distribution or p-box and can handle multivariate p-boxes with a consonant approximation method.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
Article
Computer Science, Artificial Intelligence
Laszlo Csato
Summary: This paper studies a special type of incomplete pairwise comparison matrices and proposes a new method to determine the missing elements without violating the ordinal property.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2024)
