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Öğe A Generalized f-Divergence With Applications in Pattern Classification(IEEE Computer Society, 2025) Xiao, Fuyuan; Ding, Weiping; Pedrycz, WitoldIn multisource information fusion (MSIF), Dempster-Shafer evidence (DSE) theory offers a useful framework for reasoning under uncertainty. However, measuring the divergence between belief functions within this theory remains an unresolved challenge, particularly in managing conflicts in MSIF, which is crucial for enhancing decision-making level. In this paper, several divergence and distance functions are proposed to quantitatively measure discrimination between belief functions in DSE theory, including the reverse evidential KullbackLeibler (REKL) divergence, evidential Jeffrey's (EJ) divergence, evidential JensenShannon (EJS) divergence, evidential χ2(Eχ2) divergence, evidential symmetric χ2 (ESχ2) divergence, evidential triangular (ET) discrimination, evidential Hellinger (EH) distance, and evidential total variation (ETV) distance. On this basis, a generalized f-divergence, also called the evidential f-divergence (Ef divergence), is proposed. Depending on different kernel functions, the Ef divergence degrades into several specific classes: EKL, REKL, EJ, EJS, Eχ2 and ESχ2 divergences, ET discrimination, and EH and ETV distances. Notably, when basic belief assignments (BBAs) are transformed into probability distributions, these classes of Ef divergence revert to their classical counterparts in statistics and information theory. In addition, several Ef-MSIF algorithms are proposed for pattern classification based on the classes of Ef divergence. These Ef-MSIF algorithms are evaluated on real-world datasets to demonstrate their practical effectiveness in solving classification problems. In summary, this work represents the first attempt to extend classical f-divergence within the DSE framework, capitalizing on the distinct properties of BBA functions. Experimental results show that the proposed Ef-MSIF algorithms improve classification accuracy, with the best-performing Ef-MSIF algorithm achieving an overall performance difference approximately 1.22 times smaller than the suboptimal method and 14.12 times smaller than the worst-performing method. © 1989-2012 IEEE.Öğe A Vertical Federated Multi-View Fuzzy Clustering Method for Incomplete Data(Institute of Electrical and Electronics Engineers Inc., 2025) Li, Yan; Hu, Xingchen; Yu, Shengju; Ding, Weiping; Pedrycz, Witold; Kiat, Yeo Chai; Liu, ZhongMulti-view fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC researches. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: (1) multiple views within a single client; (2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives. © 1993-2012 IEEE.Öğe An augmented Tabu search algorithm for the green inventory-routing problem with time windows(Elsevier B.V., 2021) Alinaghian, Mahdi; Tirkolaee, Erfan Babaee; Dezaki, Zahra Kaviani; Hejazi, Seyed Reza; Ding, WeipingTransportation allocates a significant proportion of Gross Domestic Product (GDP) to each country, and it is one of the largest consumers of petroleum products. On the other hand, many efforts have been made recently to reduce Greenhouse Gas (GHG) emissions by vehicles through redesigning and planning transportation processes. This paper proposes a novel Mixed-Integer Linear Programming (MILP) mathematical model for Green Inventory-Routing Problem with Time Windows (GIRP-TW) using a piecewise linearization method. The objective is to minimize the total cost including fuel consumption cost, driver cost, inventory cost and usage cost of vehicles taking into account factors such as the volume of vehicle load, vehicle speed and road slope. To solve the problem, three meta-heuristic algorithms are designed including the original and augmented Tabu Search (TS) algorithms and Differential Evolution (DE) algorithm. In these algorithms, three heuristic methods of improved Clarke-Wright algorithm, improved Push-Forward Insertion Heuristic (PFIH) algorithm and heuristic speed optimization algorithm are also applied to deal with the routing structure of the problem. The performance of the proposed solution techniques is analyzed using some well-known test problems and algorithms in the literature. Furthermore, a statistical test is conducted to efficiently provide the required comparisons for large-sized problems. The obtained results demonstrate that the augmented TS algorithm is the best method to yield high-quality solutions. Finally, a sensitivity analysis is performed to investigate the variability of the objective function.Öğe Dual-Channel Fuzzy Interaction Information Fused Feature Selection With Fuzzy Sparse and Shared Granularities(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 14.11.2024) Ju, Hengrong; Fan, Xiaoxue; Ding, Weiping; Huang, Jiashuang; Xu, Suping; Yang, Xibei; Pedrycz, WitoldFuzzy information granularity is an effective granular computation approach for feature evaluation and selection. However, most existing methods rely on a single granulation channel, neglecting different granularity representations. In this article, a novel dual-channel fuzzy interaction information fused feature selection with fuzzy sparse and shared granularities is proposed. It mainly comprises the following three parts. First, a dual-channel framework is introduced to construct the fuzzy information granularity from two different strategies. One channel employs sparse mutual strategy to form the sparse representation-based fuzzy information granularity, while the other constructs the fuzzy shared information granularity with a novel fuzzy semi-ball. Second, in each channel, the criteria of maximum relevancy, minimum redundancy, and maximum interaction is adopted to access feature correlation and perform feature ranking. Third, the two feature sequences derived from the dual-channel are fused to form a final feature sequence based on the within-class and between-class mechanism. To validate the efficacy of the proposed method, experimental validations on 15 datasets and schizophrenia data are conducted. The results show that the proposed method outperforms other algorithms in classification accuracy and statistical analysis. Moreover, its superiority regarding accuracy can be demonstrated in the experiments of schizophrenia detection, where it performs well in recognizing schizophrenia through visual interpretation.Öğe Fuzzy Granule Density-Based Outlier Detection With Multi-Scale Granular Balls(IEEE Computer Society, 2025) Gao, Can; Tan, Xiaofeng; Zhou, Jie; Ding, Weiping; Pedrycz, WitoldOutlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. © 1989-2012 IEEE.Öğe An integrated decision support framework for resilient vaccine supply chain network design(Pergamon-Elsevier Science Ltd, 2023) Tirkolaee, Erfan Babaee; Torkayesh, Ali Ebadi; Tavana, Madjid; Goli, Alireza; Simic, Vladimir; Ding, WeipingDesigning resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Nondominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the & epsilon;-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.Öğe Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka(Institute of Electrical and Electronics Engineers Inc., 2024) Malysiak Mrozek, Bozena; Ryba, Bartlomiej; Moleda, Marek; Hung, Che Lun; Pedrycz, Witold; Ding, Weiping; Mrozek, DariuszIn industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine. © 1993-2012 IEEE.Öğe A linear programming-based QFD methodology under fuzzy environment to develop sustainable policies in apparel retailing industry(Elsevier, 2023) Aydın, Nezir; Şeker, Şükran; Deveci, Muhammet; Ding, Weiping; Delen, DursunAs the retailing industry becomes more customer oriented, it struggles with integrating the voice-of-customers into quality development policies, determining accurate customer expectations, and understanding how to incorporate the required store attributes in retailing activities. The aim of this study is to provide managers with a more decisive and sustainable framework to fulfill customer satisfaction by determining the most essential customer needs and gain a competitive advantage by applying a benchmarking process for the whole retailing activities. To essentially support managers in determining and implementing required store attributes, this study develops a sustainable linear programming (LP) based Quality Function Deployment (QFD) methodology under IVIF-environment. The proposed method determines more accurate customer expectations (CEs) and related service requirements (SRs). Accordingly, while Clothing Quality, Price Policy, and Staff Behavior are determined as the most important CEs, Design of Customer Persona, Production Cost, and Marketing Ap-plications are obtained as the most affecting SRs. Since no specific study in the literature addresses uncertainty in CEs and SRs in the apparel retailing industry, we developed an LP-based QFD under the IVIF-environment framework, which reflects the ambiguity and vagueness of the evaluations better. Thus, this study contributes to the literature by proposing a sustainable framework for managers to make decisions that are more effective and take sustainable actions. The companies who want to get the advantage in the apparel retailing industry should follow the methodology provided within this study by adding their business specific dimensions. Lastly, to represent the validity and feasibility of the proposed approach sensitivity and comparison analysis are con-ducted. The results of comparison show that the LP based QFD method is as consistent as other method but more effective in terms of handling ambiguity and fuzziness of expert evaluations, comprehensively.Öğe MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection(Springer, 2024) Wu, Xunjin; Zhan, Jianming; Li, Tianrui; Ding, Weiping; Pedrycz, WitoldIn the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality.Öğe Multi-association evidential feature selection and its application to identifying schizophrenia(Elsevier Inc., 2024) Ju, Hengrong; Fan, Xiaoxue; Ding, Weiping; Huang, Jiashuang; Pedrycz, Witold; Yang, XibeiGranular Computing (GrC)-based feature selection can remove redundant features from a massive amount of data and improve the efficiency of information processing. However, the existing method of neighborhood-based information granule only considers the distance between samples, ignoring other significant relationships existing between them. To fill this gap, this paper proposes a novel feature selection approach based on two-step multi-association neighborhood evidence entropy. This approach is constructed in three phases. Firstly, adaptive k value corresponding to each sample in sparse representation method is determined. Sparse correlation and distance measure are fused to form a multi-association information granule. Then, the samples in the multi-association information granule are estimated and weak-related information is removed to constitute a two-step multi-association information granule. Secondly, sparse correlation information is processed using Dempster-Shafer evidence theory, and a new credibility-based function is developed. In addition, the credibility is used to construct a novel neighborhood evidence entropy, which can effectively reflect the uncertainty of data. Thirdly, the proposed neighborhood evidence entropy is applied to assess the importance of features. As a result, several vital features are selected. The experimental results on twelve datasets demonstrate that the effectiveness of the proposed method is superior to other algorithms in construction of information granules and classification accuracy, respectively. Finally, the proposed method is applied to the selection of brain regions in schizophrenia. It can effectively analyze the lesions of schizophrenia and improve the prediction of the disorder. The code is available at https://github.com/fxx-Aurora/TMAE-FS/tree/main. © 2024 Elsevier Inc.Öğe Optimization-based probabilistic decision support for assessing building information modelling (BIM) maturity considering multiple objectives(Elsevier, 2024) Chen, Zhen-Song; Wang, Zhuo-Ran; Deveci, Muhammet; Ding, Weiping; Pedrycz, Witold; Skibniewski, Miroslaw J.The phase of operation and maintenance (O&M) is the most time-consuming and cost-intensive stage in the project life cycle. However, the potential benefits of Building Information Modeling (BIM) in this phase have not been fully explored, unlike in the design and construction phases. This is particularly evident in the absence of a comprehensive assessment of its application capabilities. In light of this setting, we develop a BIM maturity assessment model (BIM MAM) for the project's O&M phase. The proposed model comprises of an assessment indicator system that facilitates experts in providing individual assessment results, and a collective opinion aggregation method in a probabilistic context based on a multi-objective optimization model that is employed to generate the ultimate collective assessment results. The established multi-objective optimization model for BIM MAM incorporates the influence of human behavior factors on the final results by introducing the fairness concern utility level as an objective. Finally, we take Wuhan Jiangxia Sewage Treatment Plant as a practical case to illustrate the effectiveness and feasibility of the proposed BIM MAM.Öğe Time series forecasting based on improved multilinear trend fuzzy information granules for convolutional neural networks(Institute of electrical and electronics engineers inc., 2025) Zhang, Ronghua; Zhan, Jianming; Ding, Weiping; Pedrycz, WitoldAlthough the construction of multilinear trend fuzzy information granules (FIG) achieves a win-win situation in terms of interpretability and trend extraction, in its second stage of segmentation, the equal-length segmentation will result in the loss of local trend. The granulation effect will further affect the forecasting performance of the time series. To this end, this article establishes a convolutional neural network (CNN) prediction method based on improved multilinear trend FIGs. First, considering the natural cycle characteristics of the time series, this article establishes a time series segmentation algorithm based on the valley points, which replaces the equal-length segmentation in the second stage of the construction of the multilinear trend FIGs, thus enhancing the interpretability of the granulation process. Later, an evaluation index of Gaussian fuzzy information granules (GLFIGs) is proposed for improving the trend extraction effect of each multilinear trend FIG. Since the multilinear trend FIGs are constructed in the natural period segment, in order to fully exploit the correlation of the corresponding positions of each granule to enhance the prediction accuracy, a GLFIG correspondence algorithm based on the segmentation and merging is introduced in this article. Finally, CNN is selected as the prediction model based on the data characteristics. We conduct experiments on six datasets and two artificial cycle datasets, and compare the constructed model with commonly used prediction models and the latest granularity model. At last, the experiments reveal that our model performs better.Öğe Ze-HFS: zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery(IEEE computer society, 2024) Yuan, Kehua; Miao, Duoqian; Pedrycz, Witold; Ding, Weiping; Zhang, HongyunKnowledge discovery of heterogeneous data is an active topic in knowledge engineering. Feature selection for heterogeneous data is an important part of effective data analysis. Although there have been many attempts to study the feature selection for heterogeneous data, there are still some challenges, such as the unbalanced problem between the stability and validity of the designed model. Hence, this paper focuses on how to design an effective and robust heterogeneous feature selection method, namely a zentropy-based uncertainty measure for heterogeneous feature selection(Ze-HFS). Different from other entropy-based uncertainty measures, the proposed method does not consider single-level information measures but systematically analyzes and integrates the information between different granular levels, which has an obvious advantage in the study of heterogeneous data knowledge discovery. Specifically, a heterogeneous distance metric is first introduced to construct heterogeneous neighborhood granules and heterogeneous neighborhood rough sets(HNRS). Then, the zentropy-based uncertainty measure is developed by analyzing the granular level structure in the HNRS model. Finally, two significant measures based on the above research are designed for heterogeneous feature selection. Compared with other state-of-the-art methods, the experimental results on 18 public datasets demonstrate the robustness and effectiveness of the proposed method.
