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
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.
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.
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.
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.
Feature Selection for Classification
M. Dash;H. Liu.
intelligent data analysis (1997)
Toward integrating feature selection algorithms for classification and clustering
Huan Liu;Lei Yu.
IEEE Transactions on Knowledge and Data Engineering (2005)
Feature Selection for Knowledge Discovery and Data Mining
Huan Liu;Hiroshi Motoda.
Feature selection for high-dimensional data: a fast correlation-based filter solution
Lei Yu;Huan Liu.
international conference on machine learning (2003)
Efficient Feature Selection via Analysis of Relevance and Redundancy
Lei Yu;Huan Liu.
Journal of Machine Learning Research (2004)
Subspace clustering for high dimensional data: a review
Lance Parsons;Ehtesham Haque;Huan Liu.
Sigkdd Explorations (2004)
Computational Methods of Feature Selection
Huan Liu;Hiroshi Motoda.
Discretization: An Enabling Technique
Huan Liu;Farhad Hussain;Chew Lim Tan;Manoranjan Dash.
Data Mining and Knowledge Discovery (2002)
Chi2: feature selection and discretization of numeric attributes
Huan Liu;R. Setiono.
international conference on tools with artificial intelligence (1995)
A probabilistic approach to feature selection - a filter solution
Huan Liu;Rudy Setiono.
international conference on machine learning (1996)
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