What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Data mining, Artificial intelligence, Machine learning, Support vector machine and Classifier are his primary areas of study.
His Data mining research is multidisciplinary, relying on both Ensemble learning, Credit card and Statistical classification.
The study incorporates disciplines such as Linear programming, Multiple criteria linear programming, Actuarial science, Linear discriminant analysis and Operations research in addition to Credit card.
His work carried out in the field of Artificial intelligence brings together such families of science as Optimization problem, Constraint and Process.
The concepts of his Machine learning study are interwoven with issues in Fuzzy set and Multiple-criteria decision analysis.
Yong Shi combines subjects such as Quadratic programming, Hyperplane, Outlier and Nonlinear system with his study of Support vector machine.
His most cited work include:
- EVALUATION OF CLASSIFICATION ALGORITHMS USING MCDM AND RANK CORRELATION (387 citations)
- A descriptive framework for the field of data mining and knowledge discovery (309 citations)
- The Role of Text Pre-processing in Sentiment Analysis (249 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Data mining, Machine learning, Support vector machine and Pattern recognition.
Yong Shi interconnects Linear programming and Natural language processing in the investigation of issues within Artificial intelligence.
His Linear programming research is under the purview of Mathematical optimization.
His work in Data mining covers topics such as Credit card which are related to areas like Project portfolio management.
His biological study spans a wide range of topics, including Quadratic programming, Hyperplane and Training set.
His research integrates issues of Knowledge management and Data science in his study of Knowledge extraction.
He most often published in these fields:
- Artificial intelligence (41.34%)
- Data mining (35.61%)
- Machine learning (24.72%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (41.34%)
- Machine learning (24.72%)
- Deep learning (2.09%)
In recent papers he was focusing on the following fields of study:
Yong Shi mainly focuses on Artificial intelligence, Machine learning, Deep learning, Support vector machine and Natural language processing.
His Artificial intelligence study frequently draws connections between adjacent fields such as Pattern recognition.
The various areas that Yong Shi examines in his Machine learning study include Embedding and Process.
His Support vector machine research incorporates elements of Mathematical optimization, Boosting, Computational intelligence and Scale.
His study focuses on the intersection of Scale and fields such as Linear classifier with connections in the field of Benchmark.
His work deals with themes such as Semantics and Word, which intersect with Natural language processing.
Between 2017 and 2021, his most popular works were:
- Enhance the Performance of Network Computation by a Tunable Weighting Strategy (62 citations)
- Graph K-means Based on Leader Identification, Dynamic Game, and Opinion Dynamics (53 citations)
- Public blockchain evaluation using entropy and TOPSIS (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Yong Shi mainly investigates Artificial intelligence, Machine learning, Deep learning, Natural language processing and Sentiment analysis.
His studies in Artificial intelligence integrate themes in fields like Task and Pattern recognition.
His study in Machine learning is interdisciplinary in nature, drawing from both Low-rank approximation, Cognitive learning and Incomplete Cholesky factorization.
His research in Deep learning intersects with topics in Computational photography, Segmentation, Image segmentation, Artificial neural network and Convolutional neural network.
His studies deal with areas such as Semantic information, Hybrid approach, Word2vec and Lexicon as well as Sentiment analysis.
His Support vector machine research is multidisciplinary, incorporating elements of Fitness function, Financial market, Inference, Bankruptcy prediction and Undersampling.
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