Yazar "Miao, Duoqian" seçeneğine göre listele

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    Fine-grained local label correlation for multi-label classification
    (Elsevier b.v., 2025) Zhao, Tianna; Zhang, Yuanjian; Miao, Duoqian; Pedrycz, Witold
    Comprehensive learning label correlation is conducive to boosting the accuracy of multi-label classification. While existing methods focus on exploring the correlation-aware original features or latent subspaces, they often overlook the role of correlation in deducing local structures. The oversight can result in suboptimal topic-based label correlation estimation and thus incur information loss. In contrast to the conventional single- granularity-based learning for local label correlation, we propose a multi-granularity correlation-based feature augmentation (MGOFA) model. MGOFA consists of three components that progressively refine the granularity of label correlation: granular-based feature augmentation for relative neighborhood-based class tendency estimation, granular-based latent topic mining for tendency-aware topic modeling, and fine-grained label correlation mining for augmented local label correlation learning. The information on neighborhood-based similarity between instances is explicitly leveraged and contributes to the model two-fold. Firstly, it induces the prototypes of latent topics, which share more knowledge with the label association. Secondly, it refines the discriminative granularity of the model by integrating it with the original features. Such a formulation simulates the viewpoint of human decision-making by automatically determining optimal solutions on both data and knowledge from coarse and refined granularity, respectively. Extensive comparisons completed often benchmarks demonstrate that MGOFA outperforms the state-of-the-art methods with satisfying convergence and sensitivity.
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    Multigranularity Data Analysis With Zentropy Uncertainty Measure for Efficient and Robust Feature Selection
    (Institute of Electrical and Electronics Engineers Inc., 2025) Yuan, Kehua; Miao, Duoqian; Pedrycz, Witold; Zhang, Hongyun; Hu, Liang
    Multigranularity data analysis has recently become an active research topic in the intelligent computing and data mining fields. Feature selection via multigranularity data analysis is an effective tool for characterizing hierarchical data and enhancing the accuracy of the results. Although the multigranularity data analysis method has been widely adopted for feature selection, existing studies still present one prevalent disadvantage: multigranularity data analysis mostly focuses on information presented at a single granularity while ignoring the hierarchical structure of multigranularity data, which is contrary to the nature of multigranularity. Hence, this article proposes a multigranularity data analysis with a zentropy uncertainty measure for efficient and robust feature selection. Specifically, a consistent degree is first introduced to obtain optimal granularity combinations and establish an efficient neighborhood model for multigranularity information processing. Then, a novel and robust uncertainty measure is developed by integrating the multigranularity information, namely the zentropy-based measure. Considering its accuracy among uncertainty measures, two important measures are further designed and applied to feature selection. Extensive experiments demonstrate that the proposed method can achieve better robustness and classification performance than other state-of-the-art methods. © 2013 IEEE.
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    SSS-Net: A shadowed-sets-based semi-supervised sample selection network for classification on noise labeled images
    (Elsevier, 2023) Cai, Kecan; Zhang, Hongyun; Pedrycz, Witold; Miao, Duoqian
    Sample selection is a fundamental technique utilized in image classification with noisy labels. A plethora of sample selection approaches published in the literature are based on a small-loss strategy, in which division thresholds are set manually and the correlation between sample losses is ignored. Furthermore, one of the most evident shortcomings of these approaches is that noisy samples with low-quality pseudo-labels can negatively impact the model resulting in poor performance. In this study, a shadowed-sets-based semi-supervised sample selection network called SSS-Net is developed to address these limitations. Our approach leverages a novel technique that combines a loss-similarity-based-clustering method (LSCM) with the shadowed-sets theory to adaptively select clean samples. We then introduce an original high-quality pseudo-label sample reselection (HPSR) strategy, which is designed through the co-training of two networks, to pick the samples with high-quality pseudo-labels. Finally, the selected samples are utilized to further train the network and complete classification. This study presents an automated approach that determines optimal division thresholds to select clean samples adaptively. Furthermore, it improves the current semi-supervised sample selection method by effectively utilizing noisy samples. The suitability and promising performance of the proposed approach are supported through experimental studies using five real-world datasets. Comparative studies involving several state-of-the-art methods are also reported. & COPY; 2023 Elsevier B.V. All rights reserved.
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    Within- cross- consensus-view representation-based multi-view multi-label learning with incomplete data
    (Elsevier, 2023) Zhu, Changming; Liu, Yanchen; Miao, Duoqian; Dong, Yilin; Pedrycz, Witold
    This article develops a multi-view multi-label learning for incomplete data which are ubiquitous with the usage of three kinds of representations including within-view representation, cross-view representation, and consensus-view representation. Different from the recent learning machines, the proposed learning machine takes the feature-oriented information, label-oriented information, and associated information between features and labels in multiple representations together and exploits the hidden useful information of available instances with the usage of instance-instance correlations, feature-feature correlations, label-label correlations, and feature-label correlations. The developed learning machine is named as within- cross-consensus view representation-based multi-view multi-label learning with incomplete data (WCC-MVML-ID). Extensive experiments on multiple multi-view and multi-label data sets with incomplete data validate the effectiveness of WCC-MVML-ID and it can be concluded that (1) WCC-MVML-ID outperforms other compared learning machines and its performances are more stable even though the missing rates of features and labels being larger; (2) compared with within-view information and consensus-view information, cross-view information is more useful for the processing problem about incomplete data; (3) WCC-MVML-ID can converge within 45 iterations.
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    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, Hongyun
    Knowledge 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.