Article
Computer Science, Information Systems
Ahmed Hamed, Ahmed Sobhy, Hamed Nassar
Summary: This approach tackles the challenges of processing a big incomplete information system by developing an efficient RST algorithm and distributing computational chores using the MapReduce framework. Experimental results validate the validity, accuracy, and efficiency of the approach, showing superior performance compared to similar approaches.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Chengling Zhang, Jinjin Li, Yidong Lin
Summary: This work introduces a novel knowledge reduction approach in incomplete information systems based on discernibility techniques in multigranulation rough set theory. By defining knowledge reduction concepts and decision functions, the study proposes a pessimistic lower (upper) approximation granular quality function.
Article
Computer Science, Information Systems
Xin Yang, Miaomiao Li, Hamido Fujita, Dun Liu, Tianrui Li
Summary: In this paper, a method for improving the performance of attribute reduction in a dynamic data environment is proposed. By combining incremental technology and accelerated reduction strategy, the method utilizes stable attribute groups and matrix-based incremental mechanisms for reduction search and dynamic attribute reduction. Experimental results demonstrate the effectiveness of the proposed method in terms of stability, computational cost, and classification accuracy.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Hao Ge, Chuanjian Yang, Yi Xu
Summary: This study focuses on dynamic approaches for efficiently updating three-way regions based on an incomplete neighborhood decision system (INDS). By utilizing matrix approaches and incremental mechanisms, the proposed algorithm outperforms traditional static algorithms and other incremental algorithms in terms of performance based on experiments with UCI datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qinghua Zhang, Chengying Wu, Shuyin Xia, Fan Zhao, Man Gao, Yunlong Cheng, Guoyin Wang
Summary: In this study, a novel granular ball computing method is proposed to address the limitations of the existing method. The proposed method introduces a quality index and an incremental mechanism, improving the learning capability and dynamic decision handling. Experimental results demonstrate the effectiveness and efficiency of the proposed method in classification tasks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Engineering, Industrial
Qiunan Meng, Xun Xu
Summary: In this paper, an incomplete covering rough set method based on object similarity is proposed to derive a cover for attribute reduction. Experimental results show that it outperforms compared rough set in factor selection accuracy and quote prediction with various proportions of missing data.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Hao Ge, Chuanjian Yang
Summary: This paper mainly investigates the dynamic changes of data in hybrid incomplete decision systems and proposes an incremental updating method for probabilistic approximations. Experimental results demonstrate that the proposed method can effectively update the knowledge for multi-level and multi-dimensional variants of objects and attributes.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Artificial Intelligence
Priyanka Das, Asit Kumar Das, Janmenjoy Nayak, Danilo Pelusi, Weiping Ding
Summary: The proposed method integrates neural network and rough set theory for clustering crime reports by identifying named entities and selecting phrases to describe each report. The phrases are vectorized and clustered using a graph-based algorithm, with an adaptive resonance theory neural network used to generate clusters. This approach adapts to dynamic data environments and has been validated with various crime report datasets, demonstrating its effectiveness compared to other clustering algorithms.
Article
Computer Science, Artificial Intelligence
Deyou Xia, Guoyin Wang, Qinghua Zhang, Jie Yang, Shuai Li, Man Gao
Summary: This study proposes a feature selection method based on rough sets and incremental learning, which shows higher efficiency in dynamic information systems. By introducing the nonincremental approximation feature selection method and the incremental theory of fuzzy knowledge distance, and developing an acceleration mechanism to eliminate redundant information granules, the study demonstrates the effectiveness and efficiency of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Muhammad Akram, Hafiza Saba Nawaz, Cengiz Kahraman
Summary: A rough set approximates a subset of a universal set based on a binary relation, and a Pythagorean fuzzy set provides information about truthness and falsity. This paper proposes a new combination of rough sets and Pythagorean fuzzy sets, called rough Pythagorean fuzzy sets, which can handle uncertainties in imprecise data. The manuscript presents a general framework for studying rough Pythagorean fuzzy approximations and discusses the properties of approximation operators induced from different binary relations. Algorithms for computing reduct family and rough Pythagorean fuzzy approximations are developed and applied to real-world examples.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Andrea Campagner, Davide Ciucci, Thierry Denoeux
Summary: This article reviews the connections between rough set theory and belief function theory, discusses their applications in knowledge representation and machine learning, and highlights the importance of their combined use.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2022)
Article
Computer Science, Information Systems
Jiaxin Zhan, Wenjie Wang, Jose Carlos R. Alcantud, Jianming Zhan
Summary: The generalized three-way decision (G3WD) theory, characterized by a trisecting-acting-outcome paradigm, needs to consider the psychological characteristics of decision-makers and address the challenge of information loss. This paper introduces a new three-way decision (3WD) approach that combines fuzzy set pair dominance degrees with the prospect theory and regret theory. Experimental analyses confirm the superiority and effectiveness of the proposed approach.
INFORMATION SCIENCES
(2022)
Review
Computer Science, Artificial Intelligence
Wanting Ji, Yan Pang, Xiaoyun Jia, Zhongwei Wang, Feng Hou, Baoyan Song, Mingzhe Liu, Ruili Wang
Summary: Feature selection is a key method for data preprocessing in data mining tasks, aiming to select a feature subset based on evaluation criteria. Fuzzy rough set theory has been proven to be ideal for dealing with uncertain information in feature selection. This article provides a comprehensive review of fuzzy rough set theory and its applications, discussing challenges in feature selection methods.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Computer Science, Artificial Intelligence
Zhiming Liu, Jinhai Li, Xiao Zhang, Xi-Zhao Wang
Summary: This article proposes a novel concept-cognitive learning method called SI2CCLM, which addresses the dependency on attribute order issue in existing methods by adopting a stochastic strategy independent of attribute order. A classification algorithm based on SI2CCLM is developed, and the analysis of the algorithm's parameters and convergence is conducted.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lei Yang, Keyun Qin, Binbin Sang, Weihua Xu, Han Yang
Summary: In this study, we propose a robust fuzzy dominance rough set model for knowledge acquisition from noisy and dynamic ordered data. The model incorporates fuzzy quantification and matrix representation, and introduces incremental approximation mechanisms for efficient computation. Experimental results demonstrate the model's robustness in dynamic data environment, and the efficiency of the incremental algorithms compared to the static algorithm.
Article
Computer Science, Artificial Intelligence
Songyu Ke, Zheyi Pan, Tianfu He, Yuxuan Liang, Junbo Zhang, Yu Zheng
Summary: Automatic neural architecture search is important for predicting spatio-temporal graphs, which are crucial for intelligent cities. However, manual design of neural networks is impractical for real-world deployments. To tackle this issue, a novel framework called AutoSTG(+) is proposed, which captures complex spatio-temporal correlations using spatial graph convolution and temporal convolution operations in the search space. Experimental results show that AutoSTG(+) can find effective network architectures and achieve up to about 20% relative improvements compared to human-designed networks.
ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ping Deng, Tianrui Li, Dexian Wang, Hongjun Wang, Hong Peng, Shi-Jinn Horng
Summary: Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. However, the optimization method using the Karush-Kuhn-Tucker (KKT) conditions is poorly scalable. In this study, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model, which decouples the elements of the matrix and combines them with a non-linear mapping function in a non-negative value domain. The objective function is optimized using the stochastic gradient descent (SGD) algorithm, and three uNMFMvC methods are constructed based on different mapping functions.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhaoyuan Wang, Shun Chen, Shenggong Ji, Zheyi Pan, Chuishi Meng, Junbo Zhang, Tianrui Li, Yu Zheng
Summary: Since renting a house is a low-frequency behavior, there is very limited data available for housing rental suggestions. This study proposes to investigate the issue using e-commerce data, as it shares the same users and can provide insights into their consumption attitudes and behaviors related to renting houses. However, integrating e-commerce data with geographic and traffic data poses a challenge due to the lack of supervised information. To address this, a pairwise approach is proposed to effectively utilize labeled users and a novel deep network called HouseCritic is developed to fuse features and evaluate user satisfaction. Experimental results using real-world data from Beijing, China demonstrate the effectiveness of the proposed approach, which is currently being deployed in an e-commerce company.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Muhammad Hafeez Javed, Zeng Yu, Tianrui Li, Noreen Anwar, Taha M. M. Rajeh
Summary: Inconsistent data and unclear labels make it difficult to learn anomalous behavior from video, leading to the trending use of deep clustering methods. This study proposes a Skeletal Based Autoencoder (SKELBA) that allows parallel processing of different types of inputs. It introduces a decoder-less deep clustering architecture and utilizes the relation between reconstruction error and minimizing the lower bound of mutual information (MI) to enhance stability and explore decoder-free systems. Experimental results on benchmark datasets demonstrate the superiority of the proposed model.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Lishan Wu, Jie Hu, Fei Teng, Tianrui Li, Shengdong Du
Summary: This paper proposes an enhanced sample building method (ESNCSE) to construct positive and negative samples for text semantic matching tasks. Positive sample pairs are generated by randomly inserting punctuation marks into the original text, aiming to add noise simply and efficiently. To expand the number of negative samples without increasing calculation cost, the momentum contrast based on the sentence embedding method with soft negative sample (SNCSE) is utilized. The experimental results show that the average Spearman correlation coefficient is 79.74% for BERT-base and 80.64% for BERT-large in text semantic similarity task.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Bo Xiong, Hongmei Chen, Tianrui Li, Xiaoling Yang
Summary: Multi-view graph clustering has attracted extensive research attention due to its ability to capture consistent and complementary information between views. However, multi-view data are mostly high-dimensional and may contain redundant and irrelevant features. In addition, the original data are often contaminated by noise and outliers, affecting the reliability of the learned affinity matrix. This study proposes a robust multi-view clustering model that combines low-dimensional and low-rank latent space learning, self-representation learning, and multi-view discrepancy induction fusion. Experimental results on benchmark datasets show that the proposed model outperforms state-of-the-art comparison models in terms of robustness and clustering performance.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xinyao Liu, Shengdong Du, Tianrui Li, Fei Teng, Yan Yang
Summary: With the rise of big data, the problem of data quality becomes more critical, especially the issue of missing values. Current research focuses on using neural network models like self-organizing mappings or automatic encoders for imputation. However, these methods struggle to discover interrelated and common features simultaneously. To address this, we propose a feature-fusion-enhanced autoencoder model for missing value filling, incorporating a hidden layer with de-tracking and radial basis function neurons to enhance feature learning. We also introduce a dynamic clustering-based missing value filling strategy for improved performance. Extensive experiments on thirteen datasets validate the effectiveness of our model.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhengji Li, Yuexin Wu, Jin Yang, Jiangchuan Chen, Tianrui Li
Summary: This study addresses three significant issues in review-based recommender systems by proposing a flexible dual-optimizer network, utilizing BERT for contextual information extraction, and incorporating a time-varying feature extraction scheme. Extensive tests on benchmark datasets demonstrate the substantial performance increase of the proposed TADO model compared to existing techniques.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Peng Xie, Minbo Ma, Tianrui Li, Shenggong Ji, Shengdong Du, Zeng Yu, Junbo Zhang
Summary: This paper presents a spatio-temporal dynamic graph relational learning model for predicting urban metro station flow. The model captures the traffic patterns of different stations using a node embedding representation module, learns dynamic spatial relationships between metro stations through a dynamic graph relationship learning module, and utilizes a transformer for long-term relationship prediction. Experimental results demonstrate the advantages of our method in urban metro flow prediction.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Yong Yang, Hongmei Chen, Yong Mi, Chuan Luo, Shi-Jinn Horng, Tianrui Li
Summary: This study proposes a multi-label feature selection method based on stable label relevance and label-specific features, which can efficiently handle large amounts of multi-label data and improve the classification performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Minbo Ma, Peng Xie, Fei Teng, Bin Wang, Shenggong Ji, Junbo Zhang, Tianrui Li
Summary: This study proposes a novel Hierarchical Spatio-temporal Graph Neural Network (HiSTGNN) that accurately predicts meteorological variables and stations over multiple time steps. The method constructs a hierarchical graph using an adaptive graph learning module, effectively capturing hidden spatial dependencies and diverse long-term trends. Dynamic interactive learning is introduced to facilitate information exchange between different hierarchical graphs.
INFORMATION SCIENCES
(2023)
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)
Proceedings Paper
Computer Science, Artificial Intelligence
Min Su, Shengdong Du, Jie Hu, Tianrui Li
Summary: This paper proposes a new time series forecasting model called GANAM, which enhances the attention mechanism module using a global residual network and utilizes the discriminator of a generative adversarial network to improve forecasting capability. The experiments have shown the effectiveness of this model.
2023 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS, ICCCBDA
(2023)
Article
Computer Science, Artificial Intelligence
Wei Chen, Hongjun Wang, Zhiguo Long, Tianrui Li
Summary: Co-clustering methods utilize the correlation between samples and attributes to explore the co-occurrence structure in data, playing a significant role in gene expression analysis, image segmentation, and document clustering. Existing bipartite graph partition-based co-clustering methods have high time complexity and the same number of row and column clusters. To address these problems, this paper proposes a novel fast flexible bipartite graph model (FBGPC) that directly constructs the bipartite graph using the original matrix and utilizes the inflation operation to partition the graph and learn the co-occurrence structure. Hierarchical clustering is then used to obtain the clustering results based on the relationship of the co-occurrence structure.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Zhaoyuan Wang, Zheyi Pan, Shun Chen, Shenggong Ji, Xiuwen Yi, Junbo Zhang, Jingyuan Wang, Zhiguo Gong, Tianrui Li, Yu Zheng
Summary: In this paper, a fine-grained, safe, and energy-efficient strategy to improve the efficiency of metro systems by dynamically scheduling dwell time for trains is proposed. A novel deep neural network called AutoDwell is introduced to tackle the challenges, optimizing the long-term rewards of dwell time settings through a reinforcement learning framework and capturing spatio-temporal correlations and interactions between trains. Extensive experiments on real-world datasets demonstrate the superior performance of AutoDwell in shortening passengers' overall travel time.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
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)
