What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Term, Preference, Rule-based machine translation and Set.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Ranking and Machine learning.
As part of one scientific family, Huchang Liao deals mainly with the area of Machine learning, narrowing it down to issues related to the Similarity, and often Similarity measure, Simple, Decision-making and Distance measures.
Along with Term, other disciplines of study including Set, Data mining, Rank and Fuzzy linguistic are integrated into his research.
His Preference research includes themes of Probabilistic logic and Management science.
As a part of the same scientific family, Huchang Liao mostly works in the field of Rule-based machine translation, focusing on Multiple-criteria decision analysis and, on occasion, Ranking and Linear programming.
His most cited work include:
- Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making (398 citations)
- Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making (398 citations)
- Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets (279 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Term, Probabilistic logic, Fuzzy logic and Preference.
Huchang Liao has researched Artificial intelligence in several fields, including Machine learning, Preference and Natural language processing.
Term overlaps with fields such as Set, Set, Rule-based machine translation, Ranking and Selection in his research.
Probabilistic logic is closely attributed to Data mining in his study.
His Fuzzy logic study combines topics from a wide range of disciplines, such as Normalization, Mathematical optimization and Operations research.
His research integrates issues of Pairwise comparison and Management science in his study of Preference.
He most often published in these fields:
- Artificial intelligence (34.15%)
- Term (33.45%)
- Probabilistic logic (25.00%)
What were the highlights of his more recent work (between 2019-2021)?
- Probabilistic logic (25.00%)
- Term (33.45%)
- Fuzzy logic (20.77%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Probabilistic logic, Term, Fuzzy logic, Artificial intelligence and Preference.
The concepts of his Probabilistic logic study are interwoven with issues in Normalization, Theoretical computer science, Dominance, Multi criteria decision and Rule-based machine translation.
His study in Rule-based machine translation is interdisciplinary in nature, drawing from both Linguistic preference relation and Management science.
His work on Fuzzy set as part of general Fuzzy logic research is often related to Supply chain management, thus linking different fields of science.
The Artificial intelligence study which covers Machine learning that intersects with Reliability.
The concepts of his Preference study are interwoven with issues in Pairwise comparison, Group and Decision analysis.
Between 2019 and 2021, his most popular works were:
- A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions (69 citations)
- DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making (49 citations)
- DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Term, Artificial intelligence, Fuzzy logic, Probabilistic logic and Set.
He regularly links together related areas like Machine learning in his Artificial intelligence studies.
His Fuzzy logic research is multidisciplinary, incorporating perspectives in Investment evaluation, Financial economics, Selection and Operations research.
His Operations research research focuses on subjects like Preference, which are linked to Data science.
Huchang Liao has included themes like Multi criteria decision and Rule-based machine translation in his Probabilistic logic study.
His Rule-based machine translation research incorporates themes from Regression and Set.
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