H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 33 Citations 7,153 131 World Ranking 6523 National Ranking 48

Research.com Recognitions

Awards & Achievements

1999 - Fellow of the American Statistical Association (ASA)

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A new approach for ranking fuzzy numbers by distance method

Ching-Hsue Cheng.
Fuzzy Sets and Systems (1998)

1010 Citations

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)

642 Citations

Fuzzy hierarchical TOPSIS for supplier selection

Jia-Wen Wang;Ching-Hsue Cheng;Kun-Cheng Huang.
soft computing (2009)

624 Citations

Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function

Ching-Hsue Cheng.
European Journal of Operational Research (1997)

598 Citations

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)

457 Citations

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)

456 Citations

Classifying the segmentation of customer value via RFM model and RS theory

Ching-Hsue Cheng;You-Shyang Chen.
Expert Systems With Applications (2009)

412 Citations

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)

364 Citations

Evaluating weapon systems using ranking fuzzy numbers

Ching-Hsue Cheng.
Fuzzy Sets and Systems (1999)

272 Citations

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)

267 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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