L
Longbing Cao
Researcher at University of Technology, Sydney
Publications - 457
Citations - 12248
Longbing Cao is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Computer science & Knowledge extraction. The author has an hindex of 50, co-authored 424 publications receiving 8731 citations. Previous affiliations of Longbing Cao include Yunnan University & Chinese Academy of Sciences.
Papers
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Journal ArticleDOI
Deep Learning for Anomaly Detection: A Review
TL;DR: A comprehensive survey of deep anomaly detection with a comprehensive taxonomy is presented in this paper, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods.
Journal ArticleDOI
Deep Learning for Anomaly Detection: A Review
TL;DR: This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods and discusses how they address the aforementioned challenges.
Proceedings ArticleDOI
Training deep neural networks on imbalanced data sets
TL;DR: A novel loss function called mean false error together with its improved version mean squared false error are proposed for the training of deep networks on imbalanced data sets and demonstrate the superiority of the proposed approach compared with conventional methods in classifying im balanced data sets on deep neural networks.
Journal ArticleDOI
Effective detection of sophisticated online banking fraud on extremely imbalanced data
TL;DR: An effective online banking fraud detection framework that synthesizes relevant resources and incorporates several advanced data mining techniques is proposed that can achieve substantially higher accuracy and lower alert volume than the latest benchmarking fraud detection system incorporating domain knowledge and traditional fraud detection methods.
Journal ArticleDOI
Data science: challenges and directions
TL;DR: While it may not be possible to build a data brain identical to a human, data science can still aspire to imaginative machine thinking.