What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Mathematical optimization, Pattern recognition, Algorithm and Feature selection.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
His work deals with themes such as Computational intelligence and Benchmark, which intersect with Mathematical optimization.
His Pattern recognition study integrates concerns from other disciplines, such as Local optimum and Encoding.
His Algorithm research includes elements of Convergence, Boosting, Speedup and Air quality index.
His study in Feature selection is interdisciplinary in nature, drawing from both Scale, Evolutionary computation, Curse of dimensionality and AdaBoost.
His most cited work include:
- A rapid learning algorithm for vehicle classification (389 citations)
- Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application. (300 citations)
- A self-adaptive artificial bee colony algorithm based on global best for global optimization (220 citations)
What are the main themes of his work throughout his whole career to date?
Yu Xue focuses on Artificial intelligence, Pattern recognition, Mathematical optimization, Algorithm and Evolutionary algorithm.
Yu Xue has researched Artificial intelligence in several fields, including Machine learning and Computer vision.
Yu Xue is studying Support vector machine, which is a component of Pattern recognition.
His Mathematical optimization study frequently involves adjacent topics like Benchmark.
Yu Xue works mostly in the field of Algorithm, limiting it down to topics relating to Cluster analysis and, in certain cases, Computational intelligence and Data mining, as a part of the same area of interest.
Yu Xue focuses mostly in the field of Evolutionary algorithm, narrowing it down to topics relating to Particle swarm optimization and, in certain cases, Image segmentation and Local optimum.
He most often published in these fields:
- Artificial intelligence (41.29%)
- Pattern recognition (21.94%)
- Mathematical optimization (21.94%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (41.29%)
- Pattern recognition (21.94%)
- Particle swarm optimization (12.90%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Particle swarm optimization, Evolutionary algorithm and Cluster analysis.
His research brings together the fields of Machine learning and Artificial intelligence.
He combines subjects such as Artificial neural network, Copula, Euclidean space and Riemannian manifold with his study of Pattern recognition.
His Particle swarm optimization study combines topics in areas such as Evolutionary computation and Local optimum.
His Evolutionary algorithm research incorporates elements of Image segmentation and Differential evolution.
His Cluster analysis research integrates issues from Computational intelligence, Norm, Angular velocity, Nonlinear system and Algorithm.
Between 2018 and 2021, his most popular works were:
- Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (59 citations)
- Fast and Accurate Classification of Time Series Data Using Extended ELM: Application in Fault Diagnosis of Air Handling Units (38 citations)
- A hybrid biogeography-based optimization and fuzzy C -means algorithm for image segmentation (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Particle swarm optimization, Algorithm, Cluster analysis, Metaheuristic and Support vector machine.
The various areas that Yu Xue examines in his Particle swarm optimization study include Artificial intelligence, Evolutionary computation, Evolutionary algorithm, Feature selection and Pattern recognition.
His research in Artificial intelligence intersects with topics in Computational complexity theory and Scale.
His Algorithm research is multidisciplinary, incorporating elements of Computational intelligence and Fuzzy logic.
Metaheuristic is a primary field of his research addressed under Mathematical optimization.
As part of the same scientific family, he usually focuses on Support vector machine, concentrating on Extreme learning machine and intersecting with Local search, Feature vector, Linear discriminant analysis, Surrogate model and Genetic algorithm.
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