What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Jiye Liang mainly focuses on Rough set, Data mining, Granular computing, Artificial intelligence and Dominance-based rough set approach.
In the subject of general Rough set, his work in Reduct is often linked to Granulation and Information system, thereby combining diverse domains of study.
Jiye Liang has included themes like Entropy, Measure and Feature selection in his Data mining study.
His Granular computing research includes themes of Fuzzy set, Theoretical computer science, Body of knowledge and Knowledge-based systems.
His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence.
His Dominance-based rough set approach study combines topics from a wide range of disciplines, such as Decision table and Decision rule.
His most cited work include:
- Positive approximation: An accelerator for attribute reduction in rough set theory (480 citations)
- MGRS: A multi-granulation rough set (435 citations)
- THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY (268 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Rough set, Artificial intelligence, Algorithm and Cluster analysis.
His Data mining research is multidisciplinary, incorporating perspectives in Time complexity, Reduction, Categorical variable and Data set.
His Rough set study frequently draws connections to other fields, such as Entropy.
He combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
His Algorithm research is multidisciplinary, incorporating elements of Soft computing and Clustering coefficient.
His work in Granular computing addresses subjects such as Theoretical computer science, which are connected to disciplines such as Lattice.
He most often published in these fields:
- Data mining (54.44%)
- Rough set (40.83%)
- Artificial intelligence (42.01%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (42.01%)
- Data mining (54.44%)
- Cluster analysis (21.30%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Data mining, Cluster analysis, Algorithm and Computational intelligence are his primary areas of study.
His work on Rough set and Granular computing as part of his general Artificial intelligence study is frequently connected to Group decision-making, thereby bridging the divide between different branches of science.
While working on this project, Jiye Liang studies both Rough set and Information system.
His Data mining study combines topics in areas such as Matrix decomposition, Sparse matrix, Categorical variable and Robustness.
His Cluster analysis study incorporates themes from Task, Heuristic, Data set and Benchmark.
The Computational intelligence study combines topics in areas such as Classifier, Fuzzy set, Support vector machine and Pattern recognition.
Between 2018 and 2021, his most popular works were:
- Intuitionistic Fuzzy Rough Set-Based Granular Structures and Attribute Subset Selection (39 citations)
- Information fusion in rough set theory : An overview (33 citations)
- Multi-granularity three-way decisions with adjustable hesitant fuzzy linguistic multigranulation decision-theoretic rough sets over two universes (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Rough set, Data mining, Fuzzy logic, Group decision-making and Reduction.
His Data mining research incorporates themes from Factorization, Recommender system, Collaborative filtering, Matrix decomposition and Sparse matrix.
His Fuzzy logic study combines topics in areas such as Relevance, Approximation algorithm, Reduct and Granular computing.
His study looks at the relationship between Reduction and fields such as Pattern recognition, as well as how they intersect with chemical problems.
His studies deal with areas such as Training set, Decision table, Object and Algorithm, Computation as well as Computational intelligence.
His Artificial intelligence research includes elements of Grading and Decision analysis.
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