D-Index & Metrics Best Publications
Computer Science
China
2022

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 110 Citations 63,021 445 World Ranking 92 National Ranking 10

Research.com Recognitions

Awards & Achievements

2022 - Research.com Computer Science in China Leader Award

2019 - Edward J. McCluskey Technical Achievement Award, IEEE Computer Society For contributions to machine learning and data mining.

2017 - Member of Academia Europaea

2016 - Fellow of the American Association for the Advancement of Science (AAAS)

2016 - ACM Fellow For contributions to machine learning and data mining.

2016 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to ensemble methods and learning from multi-labeled and partially-labeled data.

2013 - IEEE Fellow For contributions to learning systems in data mining and pattern recognition

2013 - ACM Distinguished Member

2011 - ACM Senior Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Training set and Ensemble learning. His Artificial intelligence study frequently links to other fields, such as Computer vision. Machine learning is closely attributed to Data mining in his research.

His Data mining research incorporates elements of Random forest, Support vector machine and Sample. The Pattern recognition study combines topics in areas such as Image, Set and Cluster analysis. His Cluster analysis research focuses on Algorithm and how it connects with Statistical classification.

His most cited work include:

  • Top 10 algorithms in data mining (3313 citations)
  • ML-KNN: A lazy learning approach to multi-label learning (1869 citations)
  • Ensembling neural networks: many could be better than all (1462 citations)

What are the main themes of his work throughout his whole career to date?

Zhi-Hua Zhou mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Mathematical optimization and Data mining. His Artificial intelligence research focuses on Semi-supervised learning, Ensemble learning, Artificial neural network, Training set and Supervised learning. His biological study spans a wide range of topics, including Stability and Active learning.

Machine learning connects with themes related to Task in his study. Within one scientific family, he focuses on topics pertaining to Facial recognition system under Pattern recognition, and may sometimes address concerns connected to Pattern recognition. His research on Data mining frequently links to adjacent areas such as Cluster analysis.

He most often published in these fields:

  • Artificial intelligence (60.13%)
  • Machine learning (40.51%)
  • Pattern recognition (17.07%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (60.13%)
  • Machine learning (40.51%)
  • Mathematical optimization (10.85%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Machine learning, Mathematical optimization, Deep learning and Regret. In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition. His research investigates the connection between Machine learning and topics such as Key that intersect with problems in Representation.

His research integrates issues of Margin, Margin distribution, Constraint and Selection in his study of Mathematical optimization. His Deep learning research focuses on subjects like Artificial neural network, which are linked to Leverage. His Regret research focuses on Convex optimization and how it relates to Sequence, Convex function, Measure and Convexity.

Between 2017 and 2021, his most popular works were:

  • A brief introduction to weakly supervised learning (450 citations)
  • Multi-Label Learning with Global and Local Label Correlation (79 citations)
  • Abductive learning: towards bridging machine learning and logical reasoning (45 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

Zhi-Hua Zhou mostly deals with Artificial intelligence, Machine learning, Regret, Deep learning and Mathematical optimization. Zhi-Hua Zhou has researched Artificial intelligence in several fields, including Task analysis and Pattern recognition. His Machine learning research is multidisciplinary, incorporating elements of Data modeling, Representation and Task.

His Regret research incorporates themes from Data mining, Concept drift, Reusability, Convex optimization and Upper and lower bounds. His research investigates the link between Deep learning and topics such as Artificial neural network that cross with problems in Scripting language and Human intelligence. His work is dedicated to discovering how Training set, Supervised learning are connected with Noise and other disciplines.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Top 10 algorithms in data mining

Xindong Wu;Vipin Kumar;J. Ross Quinlan;Joydeep Ghosh.
Knowledge and Information Systems (2007)

5181 Citations

ML-KNN: A lazy learning approach to multi-label learning

Min-Ling Zhang;Zhi-Hua Zhou.
Pattern Recognition (2007)

2381 Citations

Ensembling neural networks: many could be better than all

Zhi-Hua Zhou;Jianxin Wu;Wei Tang.
Artificial Intelligence (2002)

2121 Citations

Ensemble Methods: Foundations and Algorithms

Zhi-Hua Zhou.
(2012)

1916 Citations

A Review On Multi-Label Learning Algorithms

Min-Ling Zhang;Zhi-Hua Zhou.
IEEE Transactions on Knowledge and Data Engineering (2014)

1744 Citations

Exploratory Undersampling for Class-Imbalance Learning

Xu-Ying Liu;Jianxin Wu;Zhi-Hua Zhou.
systems man and cybernetics (2009)

1572 Citations

Exploratory Under-Sampling for Class-Imbalance Learning

Xu-Ying Liu;Jianxin Wu;Zhi-Hua Zhou.
international conference on data mining (2006)

1559 Citations

Isolation Forest

F.T. Liu;Kai Ming Ting;Zhi-Hua Zhou.
international conference on data mining (2008)

1375 Citations

Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

Min-Ling Zhang;Zhi-Hua Zhou.
IEEE Transactions on Knowledge and Data Engineering (2006)

1088 Citations

Training cost-sensitive neural networks with methods addressing the class imbalance problem

Zhi-Hua Zhou;Xu-Ying Liu.
IEEE Transactions on Knowledge and Data Engineering (2006)

1054 Citations

Editorial Boards

Frontiers of Computer Science
(Impact Factor: 2.669)

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