Xuebing Yang
Chinese Academy of Sciences
34 Papers
13 Citations
Xuebing Yang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Radar. The author has an hindex of 4, co-authored 11 publications.
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Papers
AMDO: An Over-Sampling Technique for Multi-Class Imbalanced Problems
TL;DR: This work uses a recently proposed over-sampling technique designed for numeric data sets, Mahalanobis Distance-based Over-Sampling (MDO), to capture the covariance structure of the minority class and to generate synthetic samples along the probability contours for learning algorithms.
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Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering
TL;DR: This paper implicitly map the raw time series space into multiple kernel spaces via elastic distance measure functions and resorts to the tensor constraint based self-representation subspace clustering approach to explore the essential low-dimensional structure of the data, as well as the high-order complementary information from different elastic kernels.
42
Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability
Jianing Xi,Daniel X M Wang,Xuebing Yang,Wensheng Zhang,Qinghua Huang +4 more
TL;DR: Wang et al. as discussed by the authors analyzed the necessity of developing explainable AI drug recommendation, and proposed an evaluation metric called traceability rate, which is calculated as the proportion of correct predictions that are traceable along the knowledge graph in all the ground truths.
23
SE-GRU: Structure Embedded Gated Recurrent Unit Neural Networks for Temporal Link Prediction
TL;DR: This work introduces a novel and elegant prediction architecture called Structure Embedded Gated Recurrent Unit (SE-GRU) neural networks, to strengthen the prediction robustness against frequency variation and occurrence delay of connections and validate the effectiveness and robustness of the proposed method.
16
Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases
Cancan Chen,Shan Zheng,Lei Guo,Xuebing Yang,Yan Song,Zhuo Li,Yanwu Zhu,Xiaoqi Liu,Qingzhuang Li,Huijuan Zhang,Ning Feng,Zuxuan Zhao,Tinglin Qiu,J. Du,Qiang Guo,Wensheng Zhang,Wenzhao Shi,Jianhui Ma,Fenglong Sun +18 more
TL;DR: Wang et al. as discussed by the authors presented a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists.