H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 55 Citations 10,307 310 World Ranking 2138 National Ranking 56

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 Fuzzy logic, Fuzzy set, Data mining, Recommender system and Artificial intelligence. Guangquan Zhang interconnects Tree, Tree structure, Mathematical optimization and Bilevel optimization in the investigation of issues within Fuzzy logic. His Fuzzy set study combines topics from a wide range of disciplines, such as Decision support system and Decision problem.

His study in the field of Concept drift is also linked to topics like Rank. His research in Recommender system intersects with topics in Service and Product. Artificial intelligence is frequently linked to Machine learning in his study.

His most cited work include:

  • Recommender system application developments (616 citations)
  • Transfer learning using computational intelligence (371 citations)
  • Multi-objective Group Decision Making: Methods, Software and Applications With Fuzzy Set Techniques (244 citations)

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

Guangquan Zhang mainly investigates Artificial intelligence, Fuzzy logic, Data mining, Machine learning and Fuzzy set. His Artificial intelligence research focuses on Domain and how it connects with Knowledge transfer. His studies deal with areas such as Algorithm, Decision support system and Mathematical optimization as well as Fuzzy logic.

Bilevel optimization, Decision model and Optimal decision is closely connected to Decision problem in his research, which is encompassed under the umbrella topic of Mathematical optimization. His Data mining study incorporates themes from Similarity and Recommender system, Information retrieval, Collaborative filtering. His Machine learning research integrates issues from Data modeling and Inference.

He most often published in these fields:

  • Artificial intelligence (28.60%)
  • Fuzzy logic (26.20%)
  • Data mining (19.60%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (28.60%)
  • Concept drift (8.40%)
  • Domain (6.40%)

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

Guangquan Zhang focuses on Artificial intelligence, Concept drift, Domain, Recommender system and Data mining. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Domain adaptation, Machine learning and Pattern recognition. Guangquan Zhang has included themes like Algorithm, Adaptation and Big data in his Concept drift study.

The Recommender system study combines topics in areas such as User experience design, Human–computer interaction and Group. His Data mining research includes elements of Artificial neural network and Training set. His Fuzzy logic study focuses on Fuzzy set in particular.

Between 2018 and 2021, his most popular works were:

  • Learning under Concept Drift: A Review (128 citations)
  • A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities (45 citations)
  • Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer (23 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Guangquan Zhang mainly focuses on Recommender system, Domain, Leverage, Data mining and Algorithm. His study on Recommender system is covered under Information retrieval. His work carried out in the field of Domain brings together such families of science as Context and Knowledge transfer.

His Leverage research is multidisciplinary, incorporating perspectives in Domain adaptation, Fuzzy rule and Fuzzy logic. The concepts of his Algorithm study are interwoven with issues in Classifier and Statistical hypothesis testing. The subject of his Fuzzy control system research is within the realm of Artificial intelligence.

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.

Top Publications

Recommender system application developments

Jie Lu;Dianshuang Wu;Mingsong Mao;Wei Wang.
decision support systems (2015)

954 Citations

Transfer learning using computational intelligence

Jie Lu;Vahid Behbood;Peng Hao;Hua Zuo.
Knowledge Based Systems (2015)

561 Citations

Multi-objective Group Decision Making: Methods, Software and Applications With Fuzzy Set Techniques

Jie Lu;Guangquan Zhang;Da Ruan.
(2007)

361 Citations

A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises

Xiaowei Yang;Guangquan Zhang;Jie Lu;Jun Ma.
IEEE Transactions on Fuzzy Systems (2011)

250 Citations

Multi-Objective Group Decision Making: Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM)

Jie Lu;Guangquan Zhang;Da Ruan;Fengjie Wu.
Research Papers in Economics (2007)

206 Citations

A hybrid fuzzy-based personalized recommender system for telecom products/services

Zui Zhang;Hua Lin;Kun Liu;Dianshuang Wu.
Information Sciences (2013)

175 Citations

Decider: A fuzzy multi-criteria group decision support system

Jun Ma;Jie Lu;Guangquan Zhang.
Knowledge Based Systems (2010)

173 Citations

An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria

Guangquan Zhang;Jie Lu.
Group Decision and Negotiation (2003)

169 Citations

Learning under Concept Drift: A Review

Jie Lu;Anjin Liu;Fan Dong;Feng Gu.
IEEE Transactions on Knowledge and Data Engineering (2019)

166 Citations

An extended Kuhn-Tucker approach for linear bilevel programming

Chenggen Shi;Jie Lu;Guangquan Zhang.
Applied Mathematics and Computation (2005)

160 Citations

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

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