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 36 Citations 6,015 109 World Ranking 5490 National Ranking 520

Overview

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.

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

Positive approximation: An accelerator for attribute reduction in rough set theory

Yuhua Qian;Jiye Liang;Witold Pedrycz;Chuangyin Dang.
Artificial Intelligence (2010)

650 Citations

MGRS: A multi-granulation rough set

Yuhua Qian;Jiye Liang;Yiyu Yao;Chuangyin Dang.
Information Sciences (2010)

624 Citations

Incomplete Multigranulation Rough Set

Yuhua Qian;Jiye Liang;Chuangyin Dang.
systems man and cybernetics (2010)

391 Citations

A NEW METHOD FOR MEASURING UNCERTAINTY AND FUZZINESS IN ROUGH SET THEORY

Jiye Liang;Kwai-Sang Chin;Chuangyin Dang;Richard C. M. Yam.
International Journal of General Systems (2002)

376 Citations

Stability Analysis of Positive Switched Linear Systems With Delays

Xingwen Liu;Chuangyin Dang.
IEEE Transactions on Automatic Control (2011)

272 Citations

A Group Incremental Approach to Feature Selection Applying Rough Set Technique

Jiye Liang;Feng Wang;Chuangyin Dang;Yuhua Qian.
IEEE Transactions on Knowledge and Data Engineering (2014)

265 Citations

Interval ordered information systems

Yuhua Qian;Jiye Liang;Chuangyin Dang.
Computers & Mathematics With Applications (2008)

205 Citations

An efficient accelerator for attribute reduction from incomplete data in rough set framework

Yuhua Qian;Jiye Liang;Witold Pedrycz;Chuangyin Dang.
Pattern Recognition (2011)

201 Citations

Set-valued ordered information systems

Yuhua Qian;Chuangyin Dang;Jiye Liang;Dawei Tang.
Information Sciences (2009)

199 Citations

An efficient rough feature selection algorithm with a multi-granulation view

Jiye Liang;Feng Wang;Chuangyin Dang;Yuhua Qian.
International Journal of Approximate Reasoning (2012)

197 Citations

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