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Zhengya Sun

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3 papers

1 author row

EAAI Journal 2024 Journal Article

Dynamic graphs attention for ocean variable forecasting

Junhao Wang
Zhengya Sun
Chunxin Yuan
Wenhui Li
An-An Liu
Zhiqiang Wei
Bo Yin

Forecasting the ocean dynamics is a critical issue for a wide array of climate extremes and environmental crisis. The dynamic variations are traditionally approached by relying on numerical models with all the related physical processes identified beforehand. An efficient alternative forecasting approach is based on the data-driven models. Despite their potential ability in modeling spatio-temporal ocean data, they ignore the fact that the ocean variables in different spatial regions and time periods typically have ever changing influences on each other, thus cannot yield satisfactory prediction results. In this paper, we develop a novel attention based dynamic graph for the ocean variable forecasting problem, which captures both the spatial and temporal dependencies. Specifically, we employ joint self-attention to incorporate information from the spatial graph over the target region, and model the graph evolution across long-range time steps. The performance of the proposed prediction model has been examined in the Indian Ocean based on ocean grid data products datasets. Experimental results demonstrate that this model has significant forecasting capability within 12 months, compared with the numerical methods and the state-of-the-art spatio-temporal embedding baselines.

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JBHI Journal 2020 Journal Article

Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks

Jinghao Niu
Yongqiang Tang
Zhengya Sun
Wensheng Zhang

This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed for ECG is introduced, which can jointly represent the morphology and rhythm of the heartbeat and alleviate the influence of inter-patient variation through baseline correction. The symbolic representation of the heartbeat is used by a multi-perspective convolutional neural network (MPCNN) to learn features automatically and classify the heartbeat. We evaluate our method for the detection of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) on MIT-BIH arrhythmia dataset. Compared with the state-of-the-art methods based on manual features or deep learning models, our method shows superior performance: the overall accuracy of 96. 4%, F1 scores for SVEB and VEB of 76. 6% and 89. 7%, respectively. The ablation study on our method validates the effectiveness of the proposed symbolization approach and joint representation architecture, which can help the deep learning model to learn more general features and improve the ability of generalization for unseen patients. Because our method achieves a competitive inter-patient heartbeat classification performance without complex handcrafted features or the intervention of the human expert, it can also be adjusted to handle various other tasks relative to ECG classification.

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TCS Journal 2012 Journal Article

Generic subset ranking using binary classifiers

Zhengya Sun
Wei Jin
Jue Wang

A widespread idea to attack the ranking problem is by reducing it into a set of binary preferences and applying well studied classification methods. In particular, we consider this reduction for generic subset ranking, which is based on minimization of position-sensitive loss functions. The basic question addressed in this paper relates to whether an accurate classifier would transfer directly into a good ranker. We propose a consistent reduction framework guaranteeing that the minimal regret of zero for subset ranking is achievable by learning binary preferences assigned with importance weights. This fact allows us to further develop a novel upper bound on the subset ranking regret in terms of binary regrets. We show that their ratio can be at most 2 times the maximal deviation of discounts between adjacent positions. We also present a refined version of this bound when only the quality over the top rank positions is of concern. These bounds provide theoretical support on the use of the resulting binary classifiers for solving the subset ranking problem.

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