What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
Yen-Liang Chen focuses on Data mining, Artificial intelligence, Association rule learning, Combinatorics and Pattern recognition.
The study incorporates disciplines such as Genetic algorithm and The Internet in addition to Data mining.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Scalability.
His research in Association rule learning focuses on subjects like Information extraction, which are connected to Affinity analysis.
His work in the fields of Combinatorics, such as Graph theory, intersects with other areas such as Sink and Similar time.
Yen-Liang Chen works mostly in the field of Pattern recognition, limiting it down to topics relating to Interval and, in certain cases, Temporal database, Fuzzy set, Fuzzy logic and Database, as a part of the same area of interest.
His most cited work include:
- The quickest path problem (214 citations)
- A group recommendation system with consideration of interactions among group members (139 citations)
- Opinion mining from online hotel reviews A text summarization approach (138 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Data mining, Artificial intelligence, Machine learning, Association rule learning and Information retrieval.
His work carried out in the field of Data mining brings together such families of science as Information extraction, Scalability and Cluster analysis.
His study looks at the intersection of Artificial intelligence and topics like Pattern recognition with Interval.
His research in Machine learning intersects with topics in Classifier and Data set.
His Association rule learning research includes elements of Fuzzy set, Database transaction and Node.
His study looks at the relationship between Information retrieval and fields such as Ranking, as well as how they intersect with chemical problems.
He most often published in these fields:
- Data mining (43.22%)
- Artificial intelligence (31.36%)
- Machine learning (21.19%)
What were the highlights of his more recent work (between 2015-2021)?
- Data mining (43.22%)
- Artificial intelligence (31.36%)
- Machine learning (21.19%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Data mining, Artificial intelligence, Machine learning, Cluster analysis and Information retrieval.
His study brings together the fields of Spatial clustering and Data mining.
His work in the fields of Categorization overlaps with other areas such as Early prediction.
In the subject of general Machine learning, his work in Decision tree, Decision tree model, Decision tree learning and ID3 algorithm is often linked to Popularity, thereby combining diverse domains of study.
His Decision tree study integrates concerns from other disciplines, such as Tree and Training set.
His Cluster analysis study combines topics in areas such as Content analysis and Pattern recognition.
Between 2015 and 2021, his most popular works were:
- Opinion mining from online hotel reviews A text summarization approach (138 citations)
- Emotion classification of YouTube videos (31 citations)
- Identifying conflict patterns to reach a consensus – A novel group decision approach (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Data mining
His main research concerns Information retrieval, Data mining, Artificial intelligence, Data science and Ranking.
His work on Recommender system and Recommendation model as part of general Information retrieval study is frequently linked to Basis and Normalized Google distance, therefore connecting diverse disciplines of science.
His specific area of interest is Data mining, where Yen-Liang Chen studies ID3 algorithm.
His Emotion classification and Categorization study in the realm of Artificial intelligence connects with subjects such as Sadness and Happiness.
His Data science investigation overlaps with Group, Management science and Group decision-making.
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