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 118 Citations 69,883 566 World Ranking 56 National Ranking 37

Research.com Recognitions

Awards & Achievements

2019 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to feature selection and social computing.

2018 - Fellow of the American Association for the Advancement of Science (AAAS)

2018 - ACM Fellow For contributions in feature selection for data mining and knowledge discovery and in social computing

2012 - IEEE Fellow For contributions to feature selection in data mining and knowledge discovery

2010 - ACM Distinguished Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Artificial intelligence, Feature selection, Social media, Data mining and Machine learning are his primary areas of study. Artificial intelligence is closely attributed to Pattern recognition in his study. The Feature selection study combines topics in areas such as Data pre-processing, Feature, Curse of dimensionality and Dimensionality reduction.

His Social media research includes themes of Data science and Social network. His Data mining research incorporates themes from Clustering high-dimensional data and Feature vector. His Machine learning research is multidisciplinary, incorporating perspectives in Field, Dynamic network analysis, Principal component analysis and Organizational network analysis.

His most cited work include:

  • Feature Selection for Classification (2327 citations)
  • Toward integrating feature selection algorithms for classification and clustering (1974 citations)
  • Feature selection for high-dimensional data: a fast correlation-based filter solution (1620 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Social media, Machine learning, Data science and Data mining. His Artificial intelligence study frequently draws connections between adjacent fields such as Pattern recognition. His Social media study integrates concerns from other disciplines, such as Popularity, Fake news, Internet privacy and Social network.

His Internet privacy research incorporates elements of Misinformation and Disinformation. His Data mining research focuses on Knowledge extraction in particular. His research integrates issues of Curse of dimensionality and Dimensionality reduction in his study of Feature selection.

He most often published in these fields:

  • Artificial intelligence (34.79%)
  • Social media (33.24%)
  • Machine learning (19.86%)

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

  • Social media (33.24%)
  • Artificial intelligence (34.79%)
  • Internet privacy (12.25%)

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

His scientific interests lie mostly in Social media, Artificial intelligence, Internet privacy, Machine learning and Fake news. His Social media research is multidisciplinary, incorporating elements of Misinformation, Session, Data science and Social network. His Artificial intelligence research integrates issues from Graph neural networks, Causal inference and Pattern recognition.

His studies deal with areas such as Disinformation, Social media mining, Social environment and Key as well as Internet privacy. His Recommender system study, which is part of a larger body of work in Machine learning, is frequently linked to Outcome, bridging the gap between disciplines. The concepts of his Fake news study are interwoven with issues in Exploit, Feature and Information retrieval.

Between 2018 and 2021, his most popular works were:

  • Beyond News Contents: The Role of Social Context for Fake News Detection (141 citations)
  • dEFEND: Explainable Fake News Detection (110 citations)
  • Studying Fake News via Network Analysis: Detection and Mitigation (66 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His main research concerns Social media, Fake news, Artificial intelligence, Internet privacy and Data science. His biological study spans a wide range of topics, including Misinformation, Credibility, User profile and Social network. He interconnects Exploit, Feature and Information retrieval in the investigation of issues within Fake news.

His Artificial intelligence study incorporates themes from Machine learning, Focus and Natural language processing. His Machine learning research is multidisciplinary, relying on both Graph neural networks and Causal inference. Huan Liu has included themes like Spurious relationship, Public trust, Social search and Interpretation in his Data science study.

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

Feature Selection for Classification

M. Dash;H. Liu.
intelligent data analysis (1997)

3886 Citations

Toward integrating feature selection algorithms for classification and clustering

Huan Liu;Lei Yu.
IEEE Transactions on Knowledge and Data Engineering (2005)

2933 Citations

Feature Selection for Knowledge Discovery and Data Mining

Huan Liu;Hiroshi Motoda.
(1998)

2533 Citations

Feature selection for high-dimensional data: a fast correlation-based filter solution

Lei Yu;Huan Liu.
international conference on machine learning (2003)

2490 Citations

Efficient Feature Selection via Analysis of Relevance and Redundancy

Lei Yu;Huan Liu.
Journal of Machine Learning Research (2004)

2243 Citations

Subspace clustering for high dimensional data: a review

Lance Parsons;Ehtesham Haque;Huan Liu.
Sigkdd Explorations (2004)

1502 Citations

Computational Methods of Feature Selection

Huan Liu;Hiroshi Motoda.
(2007)

1355 Citations

Discretization: An Enabling Technique

Huan Liu;Farhad Hussain;Chew Lim Tan;Manoranjan Dash.
Data Mining and Knowledge Discovery (2002)

1130 Citations

Chi2: feature selection and discretization of numeric attributes

Huan Liu;R. Setiono.
international conference on tools with artificial intelligence (1995)

1055 Citations

A probabilistic approach to feature selection - a filter solution

Huan Liu;Rudy Setiono.
international conference on machine learning (1996)

951 Citations

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