What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Fuzzy logic, Fuzzy number, Fuzzy set, Stock market index and Type-2 fuzzy sets and systems.
In the field of Fuzzy logic, his study on Fuzzy set operations overlaps with subjects such as Chen.
His work on Defuzzification as part of general Fuzzy number research is often related to Interval arithmetic, thus linking different fields of science.
He has included themes like Fuzzy mathematics and Fuzzy classification in his Defuzzification study.
Ching-Hsue Cheng interconnects Failure mode and effects analysis, Reliability engineering, Fault tree analysis and Process in the investigation of issues within Fuzzy set.
His Data mining research is multidisciplinary, relying on both Linguistic value and Time series.
His most cited work include:
- A new approach for ranking fuzzy numbers by distance method (652 citations)
- Fuzzy hierarchical TOPSIS for supplier selection (407 citations)
- Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function (389 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Fuzzy logic, Artificial intelligence, Rough set and Econometrics.
In his study, Support vector machine and Random forest is strongly linked to Feature selection, which falls under the umbrella field of Data mining.
His work on Fuzzy set, Fuzzy number and Fuzzy set operations as part of general Fuzzy logic research is frequently linked to Chen, bridging the gap between disciplines.
His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
His study on Autoregressive model is often connected to Stock market index, Stock market and Listing as part of broader study in Econometrics.
His Defuzzification research integrates issues from Fuzzy mathematics and Fuzzy classification.
He most often published in these fields:
- Data mining (39.91%)
- Fuzzy logic (31.46%)
- Artificial intelligence (30.99%)
What were the highlights of his more recent work (between 2016-2021)?
- Feature selection (15.49%)
- Econometrics (19.25%)
- Rough set (23.47%)
In recent papers he was focusing on the following fields of study:
Ching-Hsue Cheng focuses on Feature selection, Econometrics, Rough set, Data mining and Artificial intelligence.
His Econometrics research is multidisciplinary, incorporating perspectives in Operational efficiency, Fuzzy logic and Time series.
Fuzzy logic and Financial market are two areas of study in which he engages in interdisciplinary work.
As a part of the same scientific family, Ching-Hsue Cheng mostly works in the field of Rough set, focusing on Decision rule and, on occasion, Quality and Intensive care medicine.
His studies in Data mining integrate themes in fields like Statistics, Missing data and Imputation.
His work in Artificial intelligence addresses issues such as Pattern recognition, which are connected to fields such as Contextual image classification and Pixel.
Between 2016 and 2021, his most popular works were:
- Fuzzy time-series model based on rough set rule induction for forecasting stock price (29 citations)
- A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method (13 citations)
- Can learning motivation predict learning achievement? A case study of a mobile game-based English learning approach (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Econometrics, Feature selection, Fuzzy logic, Time series and Mathematics education.
His Econometrics research includes elements of Genetic algorithm, Stepwise regression and Support vector machine.
He combines subjects such as Gerontology and Data collection with his study of Feature selection.
His Fuzzy logic study incorporates themes from Rule induction and Association rule learning.
His research in the fields of Learning motivation, Language acquisition and Blended learning overlaps with other disciplines such as Anxiety and Perception.
He interconnects Classifier, Segmentation, Pattern recognition and Medical imaging in the investigation of issues within Rough set.
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