What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Huiling Chen mainly focuses on Artificial intelligence, Feature selection, Pattern recognition, Mathematical optimization and Benchmark.
The various areas that Huiling Chen examines in his Artificial intelligence study include Machine learning and Medical diagnosis.
He regularly ties together related areas like Particle swarm optimization in his Feature selection studies.
The Pattern recognition study which covers Extreme learning machine that intersects with High dimensionality and Delayed treatment.
His Global optimization, Metaheuristic and Sine cosine algorithm study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Trigonometric functions, bridging the gap between disciplines.
He has included themes like Local optimum and Optimization algorithm in his Benchmark study.
His most cited work include:
- Harris hawks optimization: Algorithm and applications (565 citations)
- Evolving support vector machines using fruit fly optimization for medical data classification (252 citations)
- A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis (227 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Mathematical optimization, Machine learning, Benchmark and Feature selection are his primary areas of study.
His work deals with themes such as Particle swarm optimization and Pattern recognition, which intersect with Artificial intelligence.
His work on Multi-swarm optimization is typically connected to Bankruptcy prediction as part of general Particle swarm optimization study, connecting several disciplines of science.
His Benchmark study combines topics in areas such as Stability, Swarm intelligence and Optimization problem, Engineering optimization.
His Feature selection study incorporates themes from Ant colony optimization algorithms, Data mining, Medical diagnosis, Data set and Receiver operating characteristic.
As part of the same scientific family, Huiling Chen usually focuses on Local optimum, concentrating on Chaotic and intersecting with Algorithm.
He most often published in these fields:
- Artificial intelligence (43.15%)
- Mathematical optimization (28.93%)
- Machine learning (29.95%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (28.93%)
- Benchmark (26.40%)
- Local optimum (20.81%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Mathematical optimization, Benchmark, Local optimum, Algorithm and Optimization problem.
His studies examine the connections between Mathematical optimization and genetics, as well as such issues in Convergence, with regards to Solver.
His Benchmark study deals with the bigger picture of Artificial intelligence.
His biological study spans a wide range of topics, including Stability and Pattern recognition.
His study in Local optimum is interdisciplinary in nature, drawing from both Range, Rate of convergence, Swarm behaviour and Continuous optimization.
His Swarm behaviour study combines topics from a wide range of disciplines, such as Metaheuristic, Medical diagnosis, Particle swarm optimization, Premature convergence and Feature selection.
Between 2019 and 2021, his most popular works were:
- Slime mould algorithm: A new method for stochastic optimization (175 citations)
- Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis (145 citations)
- An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine (109 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Benchmark, Local optimum, Mathematical optimization, Algorithm and Artificial intelligence.
The Benchmark study combines topics in areas such as Optimization problem, Swarm behaviour, Convergence and Global optimization.
His work on Differential evolution as part of general Mathematical optimization study is frequently linked to Photovoltaic system, therefore connecting diverse disciplines of science.
His research integrates issues of Chaotic and Identification in his study of Algorithm.
His research in Chaotic intersects with topics in Particle swarm optimization, Bat algorithm, Key and Feature selection.
His study looks at the relationship between Artificial intelligence and topics such as Machine learning, which overlap with Classifier.
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