1 code implementation • 10 May 2024 • Tianxiang Zhan, Yuanpeng He, Zhen Li, Yong Deng
TEFN is not a model that achieves the ultimate in single aspect, but a model that balances performance, accuracy, stability, and interpretability.
Ranked #2 on Time Series Forecasting on ETTm2 (720) Multivariate
no code implementations • 9 Apr 2024 • Yuanpeng He, Lijian Li
Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation, which often fails to solve the problem related to credibility completely.
no code implementations • 9 Apr 2024 • Yuanpeng He
Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, and there is currently a lack of effective strategies that can explore the uncertain aspects of both simultaneously.
no code implementations • 15 May 2023 • Tianxiang Zhan, Yuanpeng He, Yong Deng, Zhen Li
Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system.
no code implementations • 31 Jan 2023 • Yuanpeng He
The proposed model in this paper achieves state-of-the-art on most of time series datasets provided compared with competitive modern models.
no code implementations • 1 Jan 2023 • Yuanpeng He
Capturing feature information effectively is of great importance in vision tasks.
no code implementations • 7 Nov 2021 • Tianxiang Zhan, Yuanpeng He, Hanwen Li, Fuyuan Xiao
Visibility Graph (VG) algorithm is used for time series forecasting in previous research, but the forecasting effect is not as good as deep learning prediction methods such as methods based on Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM).
no code implementations • 18 Aug 2021 • Tianxiang Zhan, Yuanpeng He, Fuyuan Xiao
The main contribution of MFF is to improve the prediction accuracy of CCI, and propose a feature fusion framework for time series.
no code implementations • 16 May 2021 • Tianxiang Zhan, Yuanpeng He, Hanwen Li, Fuyuan Xiao
The main contribution of paper is to define the integrity of the basic probability assignment then the approximate entropy of the BPA is proposed to measure the uncertainty of the integrity of the BPA.
no code implementations • 14 May 2021 • Yuanpeng He
Information management has enter a completely new era, quantum era.
no code implementations • 12 May 2021 • Yuanpeng He
In order to further embody the details of information and better conforms to situations of real world, a Markov model is introduced into the generalized evidence theory which helps extract complete information volume from evidence provided.
no code implementations • 6 Apr 2021 • Yuanpeng He
In this paper, a detailed definition of ordinal frame of discernment has been provided.
no code implementations • 1 Apr 2021 • Yuanpeng He
Therefore, in this paper, a specially designed method is designed to provide an excellent method which improves the combination of ordinal quantum evidences reasonably and reduce the effects brought by uncertainty contained in quantum information simultaneously.
no code implementations • 21 Feb 2021 • Yuanpeng He
Therefore, a novel ordinal entropy to measure uncertainties of the frame of discernment considering the order of confirmation of propositions is proposed in this paper.
no code implementations • 31 Jan 2021 • Yuanpeng He
Quantum mass function has been applied in lots of fields because of its efficiency and validity of managing uncertainties in the form of quantum which can be regarded as an extension of classical Dempster-Shafer (D-S) evidence theory.
no code implementations • 31 Jan 2021 • Yuanpeng He
And some other kinds of methods neglect the relationship among three variables of pythagorean fuzzy set.