What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algebra
His primary areas of investigation include Rough set, Dominance-based rough set approach, Data mining, Algorithm and Monotonic function.
His Rough set research incorporates elements of Knowledge engineering and Knowledge acquisition.
His study explores the link between Data mining and topics such as Decision rule that cross with problems in Theoretical computer science.
He combines subjects such as Structure, Generalization, Cost sensitive and Selection with his study of Algorithm.
His Monotonic function research is multidisciplinary, relying on both Entropy and Entropy.
Xibei Yang studied Reduct and Cluster analysis that intersect with Mathematical optimization.
His most cited work include:
- Combination of interval-valued fuzzy set and soft set (308 citations)
- Dominance-based rough set approach and knowledge reductions in incomplete ordered information system (151 citations)
- A comparative study of multigranulation rough sets and concept lattices via rule acquisition (125 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Rough set, Artificial intelligence, Data mining, Pattern recognition and Algorithm.
His studies in Rough set integrate themes in fields like Reduction and Binary relation.
Artificial intelligence and Machine learning are frequently intertwined in his study.
His work on Granular computing as part of general Data mining study is frequently linked to Information system, therefore connecting diverse disciplines of science.
His research in Pattern recognition intersects with topics in Facial recognition system and Supervised learning.
His biological study deals with issues like Cluster analysis, which deal with fields such as Core.
He most often published in these fields:
- Rough set (56.10%)
- Artificial intelligence (48.17%)
- Data mining (34.76%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (48.17%)
- Reduct (24.39%)
- Reduction (26.83%)
In recent papers he was focusing on the following fields of study:
Xibei Yang mainly investigates Artificial intelligence, Reduct, Reduction, Rough set and Data mining.
His Artificial intelligence research includes themes of Machine learning and Pattern recognition.
His Pattern recognition course of study focuses on Fuzzy logic and Noise.
He interconnects Stability and Binary relation in the investigation of issues within Reduct.
The various areas that Xibei Yang examines in his Rough set study include Relation and Cluster analysis.
His work in the fields of Granular computing overlaps with other areas such as Granularity.
Between 2018 and 2021, his most popular works were:
- Pseudo-label neighborhood rough set: Measures and attribute reductions (33 citations)
- Pseudo-label neighborhood rough set: Measures and attribute reductions (33 citations)
- Active Learning From Imbalanced Data: A Solution of Online Weighted Extreme Learning Machine (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algebra
Xibei Yang mainly focuses on Rough set, Artificial intelligence, Reduct, Pattern recognition and Data mining.
His study on Rough set is intertwined with other disciplines of science such as Process, Constraint and Construct.
His Feature vector and Curse of dimensionality study in the realm of Artificial intelligence interacts with subjects such as Quality and Relative density.
His biological study spans a wide range of topics, including Information extraction and Relation.
His Data mining study integrates concerns from other disciplines, such as Classifier and Computational intelligence.
His work carried out in the field of Granular computing brings together such families of science as Fuzzy set, Fuzzy logic and Computation.
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