What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Tianrui Li mainly investigates Data mining, Rough set, Knowledge extraction, Dominance-based rough set approach and Set.
His Data mining research incorporates themes from Approximation algorithm, Volume, Reduction and Information system.
His Rough set research is covered under the topics of Artificial intelligence and Machine learning.
His Knowledge extraction research includes themes of Object, Theoretical computer science, Granular computing and Runtime system.
His studies deal with areas such as Logical matrix, Dynamic data, Optimal decision, Decision analysis and Decision rule as well as Dominance-based rough set approach.
His biological study spans a wide range of topics, including Boundary and Feature selection.
His most cited work include:
- A rough sets based characteristic relation approach for dynamic attribute generalization in data mining (241 citations)
- Forecasting Fine-Grained Air Quality Based on Big Data (196 citations)
- Three-way Investment Decisions with Decision-theoretic Rough Sets (156 citations)
What are the main themes of his work throughout his whole career to date?
Tianrui Li spends much of his time researching Artificial intelligence, Data mining, Rough set, Machine learning and Algorithm.
Tianrui Li focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Pattern recognition and, in some cases, Image.
Tianrui Li interconnects Variation, Reduction, Fuzzy logic, Feature selection and Data set in the investigation of issues within Data mining.
Tianrui Li has researched Rough set in several fields, including Set, Knowledge extraction, Information system and Matrix.
His study looks at the relationship between Knowledge extraction and fields such as Theoretical computer science, as well as how they intersect with chemical problems.
His research integrates issues of Decision tree and Mathematical optimization in his study of Dominance-based rough set approach.
He most often published in these fields:
- Artificial intelligence (37.08%)
- Data mining (36.36%)
- Rough set (33.97%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (37.08%)
- Data mining (36.36%)
- Pattern recognition (14.83%)
In recent papers he was focusing on the following fields of study:
Tianrui Li mainly investigates Artificial intelligence, Data mining, Pattern recognition, Cluster analysis and Machine learning.
His work on Rough set is typically connected to Focus as part of general Data mining study, connecting several disciplines of science.
His research in Rough set intersects with topics in Matrix, Conditional entropy, Categorical variable, Fuzzy logic and Information system.
His research investigates the link between Pattern recognition and topics such as Outlier that cross with problems in Anomaly detection.
In his research, Factorization is intimately related to Matrix decomposition, which falls under the overarching field of Cluster analysis.
His Machine learning research is multidisciplinary, relying on both Software bug, Task, Process and Big data.
Between 2019 and 2021, his most popular works were:
- UniViLM: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation. (32 citations)
- A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning (31 citations)
- Multivariate time series forecasting via attention-based encoder–decoder framework (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Data mining, Rough set and Deep learning.
His study in the fields of Feature, Feature learning and Transformer under the domain of Artificial intelligence overlaps with other disciplines such as Population.
His Machine learning study combines topics in areas such as Language model, Software bug, Representation, Process and Big data.
His research in Data mining is mostly concerned with Granular computing.
His study in Rough set is interdisciplinary in nature, drawing from both Matrix, Set, Algorithm, Greedy algorithm and Feature selection.
The various areas that Tianrui Li examines in his Deep learning study include Multivariate statistics, Convolutional neural network and Time series.
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