What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Data mining, Artificial intelligence, Machine learning, Ensemble learning and Artificial neural network.
His Data mining study incorporates themes from Risk analysis, Financial risk management, Risk management and Time series.
His Risk management course of study focuses on Fuzzy set and Multiple-criteria decision analysis, Operations research and Credit risk.
Support vector machine and Ensemble forecasting are among the areas of Machine learning where Lean Yu concentrates his study.
His work carried out in the field of Ensemble learning brings together such families of science as Volatility, West Texas Intermediate, Hilbert–Huang transform and Radial basis function.
His work in Artificial neural network tackles topics such as Nonlinear system which are related to areas like Principal component analysis.
His most cited work include:
- Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (448 citations)
- Credit risk assessment with a multistage neural network ensemble learning approach (225 citations)
- A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates (219 citations)
What are the main themes of his work throughout his whole career to date?
Lean Yu mostly deals with Artificial intelligence, Artificial neural network, Machine learning, Data mining and Support vector machine.
His study in the field of Ensemble learning and Ensemble forecasting also crosses realms of Generalization.
The Artificial neural network study combines topics in areas such as Foreign exchange rates, Exchange rate, Econometrics and Nonlinear system.
His Machine learning research is multidisciplinary, relying on both Classifier and Sampling.
His Data mining research is multidisciplinary, incorporating elements of Risk management, Sample, Benchmark and Time series.
In Support vector machine, Lean Yu works on issues like Credit risk, which are connected to Risk analysis, Financial risk management, Fuzzy set and Decision support system.
He most often published in these fields:
- Artificial intelligence (39.80%)
- Artificial neural network (34.33%)
- Machine learning (33.83%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (39.80%)
- Machine learning (33.83%)
- Credit risk (16.92%)
In recent papers he was focusing on the following fields of study:
Lean Yu mainly focuses on Artificial intelligence, Machine learning, Credit risk, Econometrics and Ensemble learning.
His study in the fields of Classifier, Support vector machine and Fuzzy logic under the domain of Artificial intelligence overlaps with other disciplines such as Discriminatory power and Noise level.
Lean Yu combines subjects such as Fuzzy set, Constraint and Robustness with his study of Support vector machine.
His work deals with themes such as Loan, Particle swarm optimization and Peer-to-peer, which intersect with Credit risk.
The Econometrics study which covers Index that intersects with Ranking, Preference and Data analysis.
His study in Ensemble learning is interdisciplinary in nature, drawing from both Resampling, Deep belief network, Computational intelligence and Imbalanced data.
Between 2017 and 2021, his most popular works were:
- Online big data-driven oil consumption forecasting with Google trends (63 citations)
- A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data (43 citations)
- A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting (25 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
His primary areas of study are Ensemble learning, Computational intelligence, Econometrics, Data mining and Benchmark.
Part of his project on Ensemble learning includes research on Machine learning and Artificial intelligence.
His research in Machine learning is mostly concerned with Artificial neural network.
In general Econometrics, his work in Copula, Volatility and Autoregressive conditional heteroskedasticity is often linked to Economic recovery linking many areas of study.
His Data mining study deals with Hilbert–Huang transform intersecting with Empirical research, Sample, Energy and Ensemble forecasting.
His Benchmark research focuses on Quantile and how it relates to Support vector machine.
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