Yazar "Hu, Xingchen" seçeneğine göre listele

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    A Design of Fuzzy Rule-Based Classifier for Multiclass Classification and Its Realization in Horizontal Federated Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hu, Xingchen; Zhu, Xiubin; Yang, Lan; Pedrycz, Witold; Li, Zhiwu
    Pattern recognition plays an important role in the process of knowledge discovery. The construction of easily describable and interpretable classification rules is of vital importance in pattern recognition. In this study, we propose a development of fuzzy rule-based classifier for multiclass classification problems and elaborate on a privacy-preserving realization of the proposed methodology in the presence of decentralized datasets. Fuzzy rule-based models provide an effective and efficient alternative for characterizing the complex relationship between the input variables and target classes. An overall design process of the proposed classifier consists of two main phases: (a) formation of information granules (clusters) to reveal the underlying structure of the training data, and (b) construction of local classification rules whose outputs reflect the probability distribution of the input data over all the classes. The constructed information granules form a backbone of the architecture of the classifier while the optimization of the parameters of local rules is carried out through using a gradient descent method with the guidance of the cross-entropy loss function. Furthermore, a federated gradient-based optimization mechanism is utilized to construct fuzzy classifier in a privacy-preserving approach. The originalities of the proposed methodology are twofold: first, a design of fuzzy classifier through the synergy of cluster-centric architecture and the cross-entropy loss function is presented. Second, we augment the proposed fuzzy classifier based on the concept of federated learning such that it can learn from distributed data without sacrificing data security and confidentiality. Experiments are carried out on a two-dimensional synthetic dataset and a number of real-world datasets. Experimental results show the excellent classification capability of the proposed classifier realized in the centralized way and in the federated learning environment. © 1993-2012 IEEE.
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    A Design of Fuzzy Rule-Based Classifier for Multiclass Classification and Its Realization in Horizontal Federated Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hu, Xingchen; Zhu, Xiubin; Yang, Lan; Pedrycz, Witold; Li, Zhiwu
    Pattern recognition plays an important role in the process of knowledge discovery. The construction of easily describable and interpretable classification rules is of vital importance in pattern recognition. In this study, we propose a development of fuzzy rule-based classifier for multiclass classification problems and elaborate on a privacy-preserving realization of the proposed methodology in the presence of decentralized datasets. Fuzzy rule-based models provide an effective and efficient alternative for characterizing the complex relationship between the input variables and target classes. An overall design process of the proposed classifier consists of two main phases: (a) formation of information granules (clusters) to reveal the underlying structure of the training data, and (b) construction of local classification rules whose outputs reflect the probability distribution of the input data over all the classes. The constructed information granules form a backbone of the architecture of the classifier while the optimization of the parameters of local rules is carried out through using a gradient descent method with the guidance of the cross-entropy loss function. Furthermore, a federated gradient-based optimization mechanism is utilized to construct fuzzy classifier in a privacy-preserving approach. The originalities of the proposed methodology are twofold: first, a design of fuzzy classifier through the synergy of cluster-centric architecture and the cross-entropy loss function is presented. Second, we augment the proposed fuzzy classifier based on the concept of federated learning such that it can learn from distributed data without sacrificing data security and confidentiality. Experiments are carried out on a two-dimensional synthetic dataset and a number of real-world datasets. Experimental results show the excellent classification capability of the proposed classifier realized in the centralized way and in the federated learning environment. © 1993-2012 IEEE.
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    A development of fuzzy-rule-based regression models through using decision trees
    (IEEE-inst electrical electronics engineers inc, 2024) Zhu, Xiubin; Hu, Xingchen; Yang, Lan; Pedrycz, Witol; Li, Zhiwu
    This article presents a design and realization of fuzzy rule-based regression models based on standard decision trees. A two-phase design of rule-based model is offered in this study to provide a good alternative to cope with high dimensional data. We first build a standard decision tree on the basis of variables in order to discover homogeneous subsets of the data. Subsequently, a collection of fuzzy rules is induced by the decision tree with the aim of reflecting the underlying phenomenon. The calculation of membership degrees and the refinement of fuzzy rules on the basis of data located in each partition exhibit a substantial level of originality and innovation. The introduction of fuzziness into decision rules helps to characterize and quantify the continuous change of output values near the boundary areas. The constructed fuzzy rules could efficiently handle the ambiguity and vagueness in the experimental evidence and offer an accurate characterization of the nonlinearities of the input-output relationships. The developed fuzzy models could achieve much higher prediction accuracy in comparison with traditional decision trees of the same size and fuzzy rule-based models with the same number of rules. Another advantage of the proposed methodology comes with the evident readability of the formed fuzzy rules. A series of experiments is reported to demonstrate the superiority of the proposed architecture of fuzzy rule-based models over traditional fuzzy rule-based models and decision trees.
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    A Development of Fuzzy-Rule-Based Regression Models Through Using Decision Trees
    (Institute of Electrical and Electronics Engineers Inc., 2024) Zhu, Xiubin; Hu, Xingchen; Yang, Lan; Pedrycz, Witold; Li, Zhiwu
    This article presents a design and realization of fuzzy rule-based regression models based on standard decision trees. A two-phase design of rule-based model is offered in this study to provide a good alternative to cope with high dimensional data. We first build a standard decision tree on the basis of variables in order to discover homogeneous subsets of the data. Subsequently, a collection of fuzzy rules is induced by the decision tree with the aim of reflecting the underlying phenomenon. The calculation of membership degrees and the refinement of fuzzy rules on the basis of data located in each partition exhibit a substantial level of originality and innovation. The introduction of fuzziness into decision rules helps to characterize and quantify the continuous change of output values near the boundary areas. The constructed fuzzy rules could efficiently handle the ambiguity and vagueness in the experimental evidence and offer an accurate characterization of the nonlinearities of the input-output relationships. The developed fuzzy models could achieve much higher prediction accuracy in comparison with traditional decision trees of the same size and fuzzy rule-based models with the same number of rules. Another advantage of the proposed methodology comes with the evident readability of the formed fuzzy rules. A series of experiments is reported to demonstrate the superiority of the proposed architecture of fuzzy rule-based models over traditional fuzzy rule-based models and decision trees. © 1993-2012 IEEE.
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    A Granular Aggregation of Multifaceted Gaussian Process Models
    (Institute of Electrical and Electronics Engineers Inc., 2024) Yang, Lan; Zhu, Xiubin; Pedrycz, Witold; Li, Zhiwu; Hu, Xingchen
    This study focuses on the construction of granular Gaussian process models completed at different levels of granularity and the emergence of higher-type granular outputs through aggregating the individual prediction results. Each Gaussian process model is instantiated utilizing granular data (or information granules) to enhance algorithmic efficiency and can be tailored to specific levels of precision (granularity). The overall design methodology emphasizes human centricity in system modeling by focusing on both the interpretability and accuracy of the resulting models. First, clustering algorithms are applied to construct information granules that provide a comprehensive overview of the experimental evidence. As the number of information granules grows, the existing knowledge imbedded within data could be perceived and described at increased levels of details. Information granules are built in an augmented feature space constructed by concatenating the input and output variables. Next, Gaussian process models are constructed on a basis of the information granules formed at different levels of abstraction. Subsequently, the confidence intervals are transformed to intervals and the reconciliation of the predictions produced by individual models, which offer different perspectives on the system, leads to the emergence of more abstract entities (such as type-2 intervals/fuzzy sets, etc.) rather than plain numbers. The efficacy of the comprehensive model is measured by the coverage and specificity criteria of the granular outputs. Experimental studies conducted on a synthetic dataset and a number of real-world datasets validated the effectiveness and adaptability of the proposed methodology. © 1993-2012 IEEE.
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    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, Zhong
    Multi-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.
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    An Efficient Federated Multiview Fuzzy C-Means Clustering Method
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Hu, Xingchen; Qin, Jindong; Shen, Yinghua; Pedrycz, Witold; Liu, Xinwang; Liu, Jiyuan
    Multiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges for efficient data analysis. Federated learning framework enables collaborative machine learning on distributed devices while preserving privacy constraints. Even though there have been intensive algorithms on multiview fuzzy clustering, federated multiview fuzzy clustering has not been adequately investigated so far. In this study, we first develop the federated learning mode into multiview fuzzy clustering and realize the federated optimization procedure, called federated multiview fuzzy C-means clustering. Then, we design an original strategy of consensus prototype learning during federated multiview fuzzy clustering. It is termed as federated multiview fuzzy C-means consensus prototypes clustering (FedMVFPC). We also further develop the federated alternative optimization algorithm with proven convergence. This study also introduces the notion of clustering prototype communication within the federated learning framework, and integrates the clustering prototypes of different views into a unified optimization formulation. The experimental studies on various benchmark datasets demonstrate that the proposed FedMVFPC method improves the federated clustering performance and efficiency. It achieves comparable or better clustering performance against the existing state-of-the-art multiview clustering algorithms.