D-Index & Metrics Best Publications

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 57 Citations 11,969 178 World Ranking 1957 National Ranking 1058

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 Data mining, Artificial intelligence, Data stream mining, Machine learning and Outlier. Jing Gao performs multidisciplinary studies into Data mining and Graph in his work. The various areas that Jing Gao examines in his Artificial intelligence study include Social media and Pattern recognition.

The Data stream mining study combines topics in areas such as Data stream and Training set. His Cluster analysis, Ensemble learning and Recurrent neural network study, which is part of a larger body of work in Machine learning, is frequently linked to Health informatics, bridging the gap between disciplines. He works mostly in the field of Outlier, limiting it down to concerns involving Anomaly detection and, occasionally, Temporal database, Variety and Data set.

His most cited work include:

  • Outlier Detection for Temporal Data: A Survey (516 citations)
  • Multi-view clustering via joint nonnegative matrix factorization (472 citations)
  • Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints (262 citations)

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

Jing Gao spends much of his time researching Artificial intelligence, Machine learning, Data mining, Cluster analysis and Data stream mining. His Artificial intelligence research includes themes of Key and Pattern recognition. His Feature learning and Semi-supervised learning study in the realm of Machine learning interacts with subjects such as Data modeling and Health informatics.

His research in the fields of Anomaly detection overlaps with other disciplines such as Graph. His work in Data stream mining addresses subjects such as Data stream, which are connected to disciplines such as Data stream clustering, Class and Statistical classification. Jing Gao usually deals with Deep learning and limits it to topics linked to Social media and Data science.

He most often published in these fields:

  • Artificial intelligence (51.98%)
  • Machine learning (40.59%)
  • Data mining (40.10%)

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

  • Artificial intelligence (51.98%)
  • Machine learning (40.59%)
  • Information retrieval (8.91%)

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

His main research concerns Artificial intelligence, Machine learning, Information retrieval, Social media and Deep learning. His biological study focuses on Leverage. His work carried out in the field of Machine learning brings together such families of science as Matching and Benchmark.

His Information retrieval study also includes fields such as

  • E-commerce which is related to area like Information integration,
  • Artificial neural network that intertwine with fields like Adversarial system. Information integration is a subfield of Data mining that Jing Gao studies. His Social media study combines topics in areas such as Pairwise learning and Data science.

Between 2017 and 2021, his most popular works were:

  • EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (186 citations)
  • A deep learning approach for detecting traffic accidents from social media data (102 citations)
  • KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare (56 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Jing Gao mainly investigates Artificial intelligence, Machine learning, Deep learning, Reliability and Social media. His work carried out in the field of Artificial intelligence brings together such families of science as Mobile device and Pattern recognition. His study on Convolutional neural network is often connected to Health informatics as part of broader study in Machine learning.

In the subject of general Deep learning, his work in Deep belief network is often linked to Reading, thereby combining diverse domains of study. The Reliability study combines topics in areas such as Crowdsourcing and Majority rule. As part of the same scientific family, Jing Gao usually focuses on Social media, concentrating on Fake news and intersecting with Information retrieval, Leverage, Reinforcement learning, Adversarial system and Artificial neural network.

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

Outlier Detection for Temporal Data: A Survey

Manish Gupta;Jing Gao;Charu C. Aggarwal;Jiawei Han.
IEEE Transactions on Knowledge and Data Engineering (2014)

615 Citations

Multi-view clustering via joint nonnegative matrix factorization

Jing Gao;Jiawei Han;Jialu Liu;Chi Wang.
siam international conference on data mining (2013)

593 Citations

Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

Mohammad M Masud;Jing Gao;L Khan;Jiawei Han.
IEEE Transactions on Knowledge and Data Engineering (2011)

399 Citations

Knowledge transfer via multiple model local structure mapping

Jing Gao;Wei Fan;Jing Jiang;Jiawei Han.
knowledge discovery and data mining (2008)

370 Citations

Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation

Qi Li;Yaliang Li;Jing Gao;Bo Zhao.
international conference on management of data (2014)

363 Citations

A Survey on Truth Discovery

Yaliang Li;Jing Gao;Chuishi Meng;Qi Li.
Sigkdd Explorations (2016)

313 Citations

A general framework for mining concept-drifting data streams with skewed distributions

Jing Gao;Wei Fan;Jiawei Han;Philip S. Yu.
siam international conference on data mining (2007)

288 Citations

On community outliers and their efficient detection in information networks

Jing Gao;Feng Liang;Wei Fan;Chi Wang.
knowledge discovery and data mining (2010)

284 Citations

A confidence-aware approach for truth discovery on long-tail data

Qi Li;Yaliang Li;Jing Gao;Lu Su.
very large data bases (2014)

269 Citations

Graph regularized transductive classification on heterogeneous information networks

Ming Ji;Yizhou Sun;Marina Danilevsky;Jiawei Han.
european conference on machine learning (2010)

243 Citations

Best Scientists Citing Jing Gao

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 87

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 73

Latifur Khan

Latifur Khan

The University of Texas at Dallas

Publications: 65

Aidong Zhang

Aidong Zhang

University of Virginia

Publications: 40

Charu C. Aggarwal

Charu C. Aggarwal

IBM (United States)

Publications: 37

Lu Su

Lu Su

Purdue University West Lafayette

Publications: 35

Bhavani Thuraisingham

Bhavani Thuraisingham

The University of Texas at Dallas

Publications: 32

Wei Fan

Wei Fan

Tencent (China)

Publications: 27

Mihaela van der Schaar

Mihaela van der Schaar

University of Cambridge

Publications: 26

Huan Liu

Huan Liu

Arizona State University

Publications: 25

Xingquan Zhu

Xingquan Zhu

Florida Atlantic University

Publications: 24

Yun Fu

Yun Fu

Northeastern University

Publications: 24

Christos Faloutsos

Christos Faloutsos

Carnegie Mellon University

Publications: 24

Arthur Zimek

Arthur Zimek

University of Southern Denmark

Publications: 22

Yizhou Sun

Yizhou Sun

University of California, Los Angeles

Publications: 22

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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