What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary areas of study are Artificial intelligence, Pattern recognition, Feature selection, Particle swarm optimization and Evolutionary computation.
His Artificial intelligence research incorporates themes from Genetic algorithm, Machine learning and Data mining.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Decision tree, Multi-objective optimization, Encoding and Word error rate.
His Feature selection study combines topics from a wide range of disciplines, such as Feature, Data pre-processing, Statistical classification, Dimensionality reduction and Feature extraction.
The study incorporates disciplines such as Local optimum, Rough set, Fitness function, Binary number and Benchmark in addition to Particle swarm optimization.
His work carried out in the field of Evolutionary computation brings together such families of science as Selection and Curse of dimensionality.
His most cited work include:
- A Survey on Evolutionary Computation Approaches to Feature Selection (659 citations)
- Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (602 citations)
- Particle swarm optimisation for feature selection in classification (280 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Genetic programming, Machine learning, Pattern recognition and Feature selection are his primary areas of study.
His research on Artificial intelligence often connects related topics like Particle swarm optimization.
Bing Xue usually deals with Genetic programming and limits it to topics linked to Statistical classification and Naive Bayes classifier.
His research integrates issues of Classifier, Training set and Set in his study of Machine learning.
His Pattern recognition research integrates issues from Decision tree and Cluster analysis.
His research on Feature selection also deals with topics like
- Data mining that connect with fields like Filter,
- Mutual information that connect with fields like Entropy.
He most often published in these fields:
- Artificial intelligence (81.97%)
- Genetic programming (42.30%)
- Machine learning (40.98%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (81.97%)
- Machine learning (40.98%)
- Genetic programming (42.30%)
In recent papers he was focusing on the following fields of study:
Bing Xue mainly investigates Artificial intelligence, Machine learning, Genetic programming, Pattern recognition and Feature extraction.
Contextual image classification, Convolutional neural network, Evolutionary computation, Feature selection and Feature learning are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
His Feature selection study incorporates themes from Feature, Curse of dimensionality, Particle swarm optimization, Selection and Evolutionary algorithm.
His Machine learning study incorporates themes from Training set and Encoding.
His study in Genetic programming is interdisciplinary in nature, drawing from both Tree, Statistical classification, Domain knowledge and Transfer of learning.
Bing Xue has researched Pattern recognition in several fields, including Image, Noise, Set and Kernel.
Between 2019 and 2021, his most popular works were:
- Evolving Deep Convolutional Neural Networks for Image Classification (111 citations)
- Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (99 citations)
- Completely Automated CNN Architecture Design Based on Blocks (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Artificial intelligence, Genetic programming, Machine learning, Feature extraction and Feature selection.
His Artificial intelligence research incorporates elements of Set and Pattern recognition.
His Genetic programming study also includes
- Visualization which connect with Kernel,
- Tree that intertwine with fields like Feature vector and Domain.
In general Machine learning study, his work on Symbolic regression, Missing data and Transfer of learning often relates to the realm of Task analysis, thereby connecting several areas of interest.
His Feature selection research includes elements of Feature, Curse of dimensionality, Selection, Cluster analysis and Mathematical optimization.
His Evolutionary computation research includes themes of Topic model and Field.
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