Z
Zhi-Hua Zhou
Researcher at Nanjing University
Publications - 633
Citations - 64307
Zhi-Hua Zhou is an academic researcher from Nanjing University. The author has contributed to research in topics: Semi-supervised learning & Artificial neural network. The author has an hindex of 102, co-authored 626 publications receiving 52850 citations. Previous affiliations of Zhi-Hua Zhou include Michigan State University & Tokyo Institute of Technology.
Papers
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Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Proceedings ArticleDOI
Isolation Forest
TL;DR: The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement.
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ML-KNN: A lazy learning approach to multi-label learning
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance to some well-established multi- label learning algorithms.
Journal ArticleDOI
A Review On Multi-Label Learning Algorithms
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms with relevant analyses and discussions.
Journal ArticleDOI
Ensembling neural networks: many could be better than all
Zhi-Hua Zhou,Jianxin Wu,Wei Tang +2 more
TL;DR: The bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.