1999 - Fellow of the American Statistical Association (ASA)
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 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.
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.
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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A new approach for ranking fuzzy numbers by distance method
Fuzzy Sets and Systems (1998)
EVALUATING THE BEST MAIN BATTLE TANK USING FUZZY DECISION THEORY WITH LINGUISTIC CRITERIA EVALUATION
Ching-Hsue Cheng;Yin Lin.
European Journal of Operational Research (2002)
Fuzzy hierarchical TOPSIS for supplier selection
Jia-Wen Wang;Ching-Hsue Cheng;Kun-Cheng Huang.
soft computing (2009)
Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function
European Journal of Operational Research (1997)
Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly
Ming-Hung Shu;Ching-Hsue Cheng;Jing-Rong Chang.
Microelectronics Reliability (2006)
Evaluating attack helicopters by AHP based on linguistic variable weight
Ching-Hsue Cheng;Kuo-Lung Yang;Chia-Lung Hwang.
European Journal of Operational Research (1999)
Classifying the segmentation of customer value via RFM model and RS theory
Ching-Hsue Cheng;You-Shyang Chen.
Expert Systems With Applications (2009)
Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight
Don-Lin Mon;Ching-Hsue Cheng;Jiann-Chern Lin.
Fuzzy Sets and Systems (1994)
Evaluating weapon systems using ranking fuzzy numbers
Fuzzy Sets and Systems (1999)
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Ching-Hsue Cheng;Tai-Liang Chen;Hia Jong Teoh;Chen-Han Chiang.
Expert Systems With Applications (2008)
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