GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification

Xia, Shuyin; Zhang, Zhao; Li, Wenhua; Wang, Guoyin; Giem, Elisabeth; Chen, Zizhong

doi:10.1109/tkde.2020.2997039

Cited by 135 publications

(33 citation statements)

References 41 publications

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“…This is mainly because the prediction is based solely on historical flow data. If more meteorological factors affecting the flow are considered, then the proposed method is likely to yield a more accurate prediction result; in addition, if the clustering methods and feature selection methods can be combined into the proposed method, such as that in [33] and [34], the prediction performance may be further improved.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Streamflow Based on Dynamic Sliding Window LSTM

Dong

Fang

Wang

et al. 2020

Water

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The streamflow of the upper reaches of the Yangtze River exhibits different timing and periodicity characteristics in different quarters and months of the year, which makes it difficult to predict. Existing sliding window-based methods usually use a fixed-size window, for which the window size selection is random, resulting in large errors. This paper proposes a dynamic sliding window method that reflects the different timing and periodicity characteristics of the streamflow in different months of the year. Multiple datasets of different months are generated using a dynamic window at first, then the long-short term memory (LSTM) is used to select the optimal window, and finally, the dataset of the optimal window size is used for verification. The proposed method was tested using the hydrological data of Zhutuo Hydrological Station (China). A comparison between the flow prediction data and the measured data shows that the prediction method based on a dynamic sliding window LSTM is more accurate by 8.63% and 3.85% than the prediction method based on fixed window LSTM and the dynamic sliding window back-propagation neural network, respectively. This method can be generally used for the time series data prediction with different periodic characteristics.

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Section: Discussionmentioning

confidence: 99%

Prediction of Streamflow Based on Dynamic Sliding Window LSTM

Dong

Fang

Wang

et al. 2020

Water

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“…Besides the gap rough set, Xia et al [50,51] have also developed a novel concept called granular ball for adaptively acquiring the information granules of samples. Furthermore, it should be emphasized that the sizes of the information granules in terms of different samples may be different.…”

Section: Granular Ball Rough Set and Attribute Reductionmentioning

confidence: 99%

“…From discussions above, a new MGRS called Triple-G MGRS (covers three different "G", i.e., neiGhborhood, Gap and Granular ball) will be developed in the context of this paper. Different from previous MGRS, our Triple-G MGRS employs the following three different information granulations: (1) a neiGhborhood [25,46,53] based parameterized information granulation; (2) a Gap [65] based data-adaptive information granulation; (3) a Granular ball [50,51] based data-adaptive information granulation. Obviously, since both parameterized information granulation and data-adaptive information granulations have been used, our Triple-G MGRS does reflect the fundamental principle of heterogeneous information granulation.…”

Section: Introductionmentioning

confidence: 99%

Triple-G: a new MGRS and attribute reduction

Liu

et al. 2021

Int. J. Mach. Learn. & Cyber.

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Different from classical rough set, Multigranulation Rough Set (MGRS) is frequently designed for approximating target through using multiple results of information granulation. Presently, though many forms of MGRS have been intensively explored, most of them are constructed based on the homogeneous information granulation with respect to different scales or levels. They lack the multi-view which involves the results of heterogeneous information granulation. To fill such a gap, a Triple-G MGRS is developed. Such a Triple-G is composed of three different heterogeneous information granulations:(1) neiGhborhood based information granulation; (2) Gap based information granulation; (3) Granular ball based information granulation. Neighborhood provides a parameterized mechanism while gap and granular ball offer two representative data-adaptive strategies for performing information granulation. Immediately, both optimistic and pessimistic MGRS can be re-constructed. Furthermore, the problem of attribute reduction is also addressed based on the proposed models. Not only the forward greedy searching is used for deriving the Triple-G MGRS related reducts, but also an attribute grouping based accelerator is reported for further speeding up the process of searching reducts. The experimental results over 20 UCI data sets demonstrate the follows: (1) from the viewpoint of the generalization performance, the reducts obtained by our Triple-G MGRS is superior to those obtained by previous researches; (2) attribute grouping does speed up the process of searching reducts.

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“…That accelerates the innovative evolution of deep learning technology, which has demonstrated the prominent achievement in widely-applied technologies, including computer vision, nature language processing and data mining [40], [42]. From the successful applications, deep learning shows obvious superiorities over conventional hand-crafted-feature-based models [3], [43]- [45]. Several Deep learning approaches [12], [19], [39] aim to improve the identification accuracy on the fundus images to diagnose DR, and design various models to support the application in computer-aided diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification

Zhang¹,

Pan²,

Zhang

et al. 2021

IEEE Access

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Diabetic Retinopathy (DR) causes a significant health threat to the patient's vision with diabetic disease, which may result in blindness in severe situations. Various automatic DR diagnosis models have been proposed along with the development of deep learning, while there always relies on a large scale annotated data to train the network. However, annotating medical fundus images is cost-expensive and requires well-trained professional doctors to identity the DR grades. To overcome this drawback, this paper focuses on utilizing the easily-obtained unlabeled data with the help of limited annotated data to identify DR grades accurately. Hence we proposes a semi-supervised retinal image classification method by a Hybrid Graph Convolutional Network (HGCN). This HGCN network designs a modularity-based graph learning module and integrates Convolutional Neural Network (CNN) features into the graph representation by graph convolutional network. The synthesized hybrid features are optimized by a semi-supervised classification task which is assisted by a similarity-based pseudo label estimator. Through the proposed HGCN method, the retinal image classification model can be trained efficiently by partially labeled samples and the complicated annotating work is not required for the most retinal images. The experimental results on MESSIDOR dataset demonstrate the favorable performance of HGCN on semi-supervised retinal image classification, and the fully labeled data training also achieves an obvious superiority to the state-of-the-art supervised learning methods.

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GBNRS: A Novel Rough Set Algorithm for Fast Adaptive Attribute Reduction in Classification

Cited by 135 publications

References 41 publications

Prediction of Streamflow Based on Dynamic Sliding Window LSTM

Prediction of Streamflow Based on Dynamic Sliding Window LSTM

Triple-G: a new MGRS and attribute reduction

Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification

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