Article
Computer Science, Artificial Intelligence
Xunjin Wu, Jianming Zhan, Weiping Ding
Summary: This paper designs a multivariate prediction model TWC-EL model utilizing three-way clustering and ensemble learning, aiming to improve the prediction accuracy and adaptability to complex data, and reduce the impact of randomness on clustering accuracy. The sample set is initially divided using the k-means clustering algorithm and further divided again to solve the problem of clustering accuracy. The obtained core and fringe regions are classified based on the correlation between them, and an ensemble prediction model is designed. The experimental results show that the constructed TWC-EL model is efficient and feasible, and outperforms existing prediction models.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Haiyan Xu, Yuqing Chang, Yong Zhao, Fuli Wang
Summary: A new hybrid model was proposed to improve wind speed forecasting performance, which decomposed wind speed series into subseries with different frequencies and utilized various frequency components for prediction. The model showed better forecasting performance compared to 13 other models in three actual datasets, indicating its effectiveness in wind speed forecasting.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Ge Liu, Wenping Ma
Summary: Predicting stock market behavior accurately is challenging due to its high volatility. To enhance market forecasts, a method combining Elman neural network and quantum mechanics is proposed. By introducing the internally self-connected signal and employing the double chains quantum genetic algorithm to adjust learning rates, the network becomes sensitive to dynamic information. The model is validated by forecasting closing prices of six stock markets, demonstrating its feasibility and effectiveness, and suggesting potential for generalization.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qinghua Zhang, Zhikang Huang, Guoyin Wang
Summary: This paper introduces a new sequential three-way decision model with autonomous error correction to reduce error classification rate by subdividing granules. Experimental results show that the proposed model outperforms traditional models in terms of accuracy.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Ming Zhang, Jianxun Yang, Rongfu Ma, Qian Du, Dragan Rodriguez
Summary: This study investigates the lateral deflection of piles under different conditions and proposes a prediction model using the ENN-IAO algorithm. Through 192 physical model experiments considering the important factors of lateral deflection, the proposed method is found to be more reliable compared to other models.
Article
Computer Science, Information Systems
Decui Liang, Bochun Yi
Summary: This study proposes a two-stage three-way enhanced technique to automatically classify policy text paragraphs into predefined categories. Experimental results show that the proposed method effectively supports the design of policy recommended platforms and serves SMEs.
INFORMATION SCIENCES
(2021)
Article
Environmental Sciences
Chao Song, Xiaohong Chen
Summary: This paper focuses on improving precipitation prediction accuracy through the use of different decomposition methods to build prediction models, using annual precipitation in Guangzhou as a case study. The TVF-EMD-ENN model shows the best prediction performance, with secondary decomposition significantly improving accuracy.
Article
Computer Science, Interdisciplinary Applications
Ungki Lee, Namwoo Kang
Summary: This study proposes a prediction frequency-based ensemble method that identifies core prediction values with high frequency to improve the prediction accuracy of neural network models.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Thermodynamics
Lin Ding, Yulong Bai, Ming-De Liu, Man-Hong Fan, Jie Yang
Summary: A novel short-term wind speed prediction model based on double decomposition, piecewise error correction, Elman neural network, and the autoregressive integrated moving average model is proposed in this study. Experimental results show that the proposed model can improve prediction accuracy and effectively handle different types of problems.
Article
Computer Science, Artificial Intelligence
Pei Liang, Wanying Cao, Junhua Hu
Summary: This paper investigates the application of the sequential three-way decision (S3WD) model in classification and proposes sequential three-way classifiers (S3WCs) to address risk preference and decision conflict. Experimental results demonstrate the superior classification performance of the proposed models on diverse datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics
Tingfeng Wu, Jiachen Fan, Pingxin Wang
Summary: Three-way clustering shows the uncertainty information in a dataset by adding the concept of a fringe region. This paper introduces an improved three-way clustering algorithm that generates diverse base clustering results based on a feature subset of samples and traditional clustering algorithm. The proposed algorithm uses labels matching and voting method to obtain the core region and the fringe region of the three-way clustering.
Article
Chemistry, Multidisciplinary
Chenhui Wang, Yijiu Zhao, Libing Bai, Wei Guo, Qingjia Meng
Summary: The research proposes a landslide displacement prediction model based on Genetic Algorithm optimized Elman neural network, which shows high prediction accuracy through the prediction and verification of the displacement data of a slow-varying landslide in the Guizhou karst mountainous area.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Xue Wu, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li
Summary: In crowdsourcing learning, label noise exists in the integrated labels obtained by label integration algorithms. To decrease the impact of label noise, many scholars focus on noise correction algorithms. The proposed TDNC algorithm, inspired by three-way decision theory, outperforms existing noise correction algorithms in terms of noise ratio.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Article
Construction & Building Technology
Qinglong Meng, Yuan Xi, Xiaoxiao Ren, Hui Li, Le Jiang, Li Yang
Summary: This study focuses on load forecasting and demand response strategies for thermal energy storage systems. An Elman neural network prediction model and control strategy are developed and compared with other machine learning algorithms. Experimental results show that the proposed model and strategy effectively reduce energy use and operation costs.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Chemistry, Multidisciplinary
Min Li, Wei Zhou, Jiang Liu, Xilong Zhang, Fuquan Pan, Huan Yang, Mengshan Li, Dijia Luo
Summary: This study successfully predicted and reduced vehicle interior noise through the establishment of a finite element model and the use of the Elman neural network method, while also improving side impact performance. The results provide a reference method for multidisciplinary research aiming to optimize the design of vehicle body structures.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Rongtao Zhang, Xueling Ma, Jianming Zhan, Yiyu Yao
Summary: Clustering is an important method in unsupervised learning that can uncover the distribution patterns and attributes of data. Traditional clustering methods fail to fully represent the relationship between objects and classes, leading to the emergence of three-way clustering (3WC) that combines three-way decision (3WD) with clustering. However, most existing 3WC methods overlook the distribution relationship of each object and the ambiguity of clustering results. This article proposes a feature distribution-based adaptive three-way clustering (3WC-D) method to address these challenges.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jianming Zhan, Kai Zhang, Peide Liu, Witold Pedrycz
Summary: This paper explores a multi-scale group decision-making method to deal with decision-making problems with multi-scale information. The method consists of two stages, where a newly ranking decision-making approach and a decision fusion approach are introduced. The method provides theoretical and methodological support for establishing a multi-scale decision analysis system.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jiang Deng, Jianming Zhan, Weiping Ding, Peide Liu, Witold Pedrycz
Summary: This article introduces a new three-way decision model based on probability theory, aiming to solve multi-attribute decision-making problems. By designing a new value function, it can better reflect the relative position of the object and avoid the issue of subjectivity. The effectiveness and stability of the method are demonstrated through verification and analysis.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Jin Ye, Bingzhen Sun, Xiaoli Chu, Jianming Zhan, Qiang Bao, Jianxiong Cai
Summary: This article discusses the problem of multisource heterogeneous fuzzy decision making and proposes a novel group decision making method based on multigranulation rough sets. A weighted multigranulation generalized fuzzy rough set model is constructed to model the multisource heterogeneous fuzzy decision systems, and extended entropy weight methods are used to calculate the weights of attributes and experts. Finally, the method is applied to the management decision-making problem of gout diagnosis, and comparative and sensitivity analyses demonstrate the feasibility, effectiveness, stability, and superiority of the method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiang Deng, Jianming Zhan, Enrique Herrera-Viedma, Francisco Herrera
Summary: Behavioral decision theory modifies classic decision-making theories to make them more applicable in realistic scenarios. Regret theory, an important component of behavioral decision theories, has been widely used in theories and applications. In this study, a generalized three-way decision method is proposed based on regret theory for incomplete multiscale decision information systems. Experimental results show that the decision-making results of the proposed method maintain over 97% consistency in incomplete information systems.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Lun Guo, Jianming Zhan, Zeshui Xu, Jose Carlos R. Alcantud
Summary: In this paper, a novel fuzzy large group decision making method is proposed, which utilizes three-way clustering and an adaptive exit-delegation mechanism. The method separates the edge points and outliers from the clustering results using the three-way relationships, and determines the individual weight and trust weight using a consensus measure-based model. Comparative analyses verify the feasibility and effectiveness of the proposed method.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Jiang Deng, Jianming Zhan, Zeshui Xu, Enrique Herrera-Viedma
Summary: This article proposes a wide three-way decision model on multiscale information systems (MSIS), combining 3WD theory and regret theory, to address the two problems in existing MADM methods. The model effectively tackles misclassification and incorporates decision makers' risk attitudes and psychological behaviors. Experimental analysis confirms the effectiveness, superiority, and stability of the proposed model.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Xianfeng Huang, Jianming Zhan, Weiping Ding, Witold Pedrycz
Summary: This paper introduces a new multivariate fusion prediction system that addresses the issues of mining prediction information from multi-scale information systems, describing the irrational behavior of decision-makers in prediction processes, and improving the prediction performance and generalization capability of the models. The system establishes the score function of each feature under multi-scale information systems using scale rules, regret theory, and dominance relations, and develops an adaptive S3WD model for feature selection. It also develops an improved version of the kernel extreme learning machine (KELM) called IKELM to enhance its effectiveness and rationality. The experimental results demonstrate that the proposed system outperforms existing machine learning models in terms of prediction performance and generalization ability.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Jinxing Zhu, Xueling Ma, Gang Kou, Enrique Herrera-Viedma, Jianming Zhan
Summary: This paper proposes a PL-RT-GTWD model based on regret theory for multi-attribute group decision-making (MAGDM). The model includes a consensus measurement considering the relativity among decision-makers and a three-way consensus feedback mechanism with minimum adjustment. An optimization model is constructed for all decision-makers to modify and reach the final desired goal, aiming to balance consensus and individual independence. The model's feasibility and superiority are verified through comparative and sensitivity analyses.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Yu Wang, Jianming Zhan, Chao Zhang
Summary: This study introduces a three-way decision (TWD) method that incorporates prospect theory (PT) and probabilistic linguistic term sets (PLTSs) to address multi-attribute decision-making (MADM) problems. A novel distance formula is introduced to address the shortcomings of existing distance formulas for PLTSs. The weight calculation is explored from two dimensions, and a novel satisfaction function is formulated to address the limitation of reference point selections in PT.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Yufeng Shen, Xueling Ma, Jianming Zhan
Summary: With the advancement of digitalization and the evolution of societal patterns, large-scale group decision making has become increasingly important in the field of management sciences. However, existing consensus models often neglect the significance of intra-subgroup consensus degrees. Our proposed TWC-ACR model effectively tackles this issue and achieves superior decision outcomes.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Chenglong Zhu, Xueling Ma, Chao Zhang, Weiping Ding, Jianming Zhan
Summary: The BPNN model based on GTS performs better in long-term time series forecasting.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Rongtao Zhang, Xueling Ma, Weiping Ding, Jianming Zhan
Summary: Currently, prediction is a significant area of research, with the challenge of improving accuracy and generalization capabilities of models. To address the issue of error accumulation in existing prediction models, we propose a multi-step time series prediction model that incorporates prediction correction. Our model effectively rectifies initial predictions and safeguards against deviations, demonstrating its effectiveness through comparative experimental analysis.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Cybernetics
Chao Zhang, Juanjuan Ding, Jianming Zhan, Arun Kumar Sangaiah, Deyu Li
Summary: The objective of this article is to explore a fuzzy intelligence learning approach based on bounded rationality in IoMT systems for biomedical data analysis. The approach utilizes adjustable multigranulation rough sets and interactive multicriteria decision-making to detect freezing of gait in Parkinson's disease. Experimental analyses on a UCI dataset demonstrate the effectiveness of this approach in diagnosing freezing of gait.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xunjin Wu, Jianming Zhan, Weiping Ding
Summary: This paper designs a multivariate prediction model TWC-EL model utilizing three-way clustering and ensemble learning, aiming to improve the prediction accuracy and adaptability to complex data, and reduce the impact of randomness on clustering accuracy. The sample set is initially divided using the k-means clustering algorithm and further divided again to solve the problem of clustering accuracy. The obtained core and fringe regions are classified based on the correlation between them, and an ensemble prediction model is designed. The experimental results show that the constructed TWC-EL model is efficient and feasible, and outperforms existing prediction models.
INFORMATION FUSION
(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)