What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Mathematical analysis
His scientific interests lie mostly in Data mining, Rough set, Artificial intelligence, Dominance-based rough set approach and Granular computing.
He has included themes like CURE data clustering algorithm, Cluster analysis and Data set in his Data mining study.
The various areas that Chuangyin Dang examines in his Rough set study include Approximation algorithm, Reduction, Feature selection and Applied mathematics.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
His Dominance-based rough set approach research includes themes of Algorithm, Decision table and Decision rule.
The concepts of his Granular computing study are interwoven with issues in Fuzzy set, Fuzzy logic, Knowledge extraction and Knowledge-based systems.
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)
- Incomplete Multigranulation Rough Set (252 citations)
What are the main themes of his work throughout his whole career to date?
Chuangyin Dang spends much of his time researching Mathematical optimization, Algorithm, Data mining, Artificial intelligence and Control theory.
As part of his studies on Mathematical optimization, Chuangyin Dang frequently links adjacent subjects like Convergence.
His Data mining research is multidisciplinary, relying on both CURE data clustering algorithm, Data set and Cluster analysis.
His Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
His Control theory course of study focuses on Fuzzy logic and Filter, Filter design and Nonlinear system.
His Rough set study integrates concerns from other disciplines, such as Measure, Reduction and Feature selection.
He most often published in these fields:
- Mathematical optimization (28.90%)
- Algorithm (20.64%)
- Data mining (14.68%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (28.90%)
- Homotopy (7.80%)
- Applied mathematics (9.63%)
In recent papers he was focusing on the following fields of study:
Chuangyin Dang focuses on Mathematical optimization, Homotopy, Applied mathematics, Function and Computation.
His biological study spans a wide range of topics, including Conic model, Trust region and Unconstrained optimization.
He interconnects Logarithm, Numerical analysis, Subgame perfect equilibrium and Stochastic game in the investigation of issues within Applied mathematics.
His Function study also includes
- Transportation theory that connect with fields like Benchmark and Global optimal,
- Polynomial, which have a strong connection to Uniqueness, Discrete mathematics, Isotonic regression and Inequality,
- Artificial neural network that intertwine with fields like Upper and lower bounds, Graph partition, Lagrange multiplier and Combinatorial optimization.
The Penalty method study which covers Constrained optimization that intersects with Stability, Convergence and Algorithm.
His research on Granular computing concerns the broader Artificial intelligence.
Between 2016 and 2021, his most popular works were:
- Local rough set: A solution to rough data analysis in big data (50 citations)
- Attribute reduction for sequential three-way decisions under dynamic granulation (46 citations)
- An approximation algorithm for graph partitioning via deterministic annealing neural network. (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Mathematical analysis
His primary areas of investigation include Data mining, Mathematical optimization, Artificial intelligence, Cluster analysis and Resource.
His Data mining study frequently draws connections to adjacent fields such as Reduction.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Fixed point, Fixed-point iteration, Construct, Division and Computation.
His Artificial intelligence research includes elements of Structure and Pattern recognition.
He works mostly in the field of Cluster analysis, limiting it down to concerns involving Data set and, occasionally, Stability, Sample, Stratified sampling and Computational complexity theory.
His Reduct study, which is part of a larger body of work in Rough set, is frequently linked to Granularity, bridging the gap between disciplines.
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