Yazar "Li, Yan" seçeneğine göre listele

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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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    Time series prediction with granular neural networks
    (Elsevier, 2023) Song, Mingli; Li, Yan; Pedrycz, Witold
    Conventional artificial neural networks are inherently equipped with an ambiguous (uncertain) structure which is hard to be quantified and explained. Time series forecasting using neural networks thus becomes a highly challenging task also due to the fact that time series data are always nonlinear and uncertain (because of some disturbances). Considering this, we propose a granular neural network -based time series prediction model connecting the uncertainty of models and data with the concept of information granularity. We aim to provide an explainable time series prediction model to resist the dis-turbance inner time series data and reduce the vagueness of the model. The functionalities of the granular neural network model are threefold: (1) It reveals the uncertainty of a time series data set through the level of granularity, coverage and specificity and possesses high prediction accuracy; (2) It provides an opti-mized interval output endowed with enough specificity and sufficient coverage and this interval is more robust than a single value; (3) It owns an interpretable and flexible structure that reflects the uncertainty of the initial numeric neural network and the generalization and robustness of connections while being faced with disturbances. The experimental studies elaborate on each function of our model in detail and show that the developed method performed better than the existing approaches present in the literature when experimenting on several time series data sets.(c) 2023 Elsevier B.V. All rights reserved.