What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Keith C. C. Chan mainly focuses on Artificial intelligence, Data mining, Machine learning, Fuzzy logic and Association rule learning.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Cognitive computing and Pattern recognition.
His Data mining research incorporates themes from Algorithm, Representation, Missing data and Cluster analysis.
His Machine learning research is multidisciplinary, incorporating elements of Quality, Probabilistic logic and Set.
His Fuzzy logic research integrates issues from Measure, Computer network and Sensor fusion.
Keith C. C. Chan has included themes like Defuzzification, Fuzzy classification and Database design in his Association rule learning study.
His most cited work include:
- A novel evolutionary data mining algorithm with applications to churn prediction (289 citations)
- Class-dependent discretization for inductive learning from continuous and mixed-mode data (210 citations)
- Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data (186 citations)
What are the main themes of his work throughout his whole career to date?
Keith C. C. Chan mostly deals with Artificial intelligence, Data mining, Machine learning, Cluster analysis and Fuzzy logic.
In his research on the topic of Artificial intelligence, Protein sequencing is strongly related with Pattern recognition.
His research in Data mining tackles topics such as Gene which are related to areas like Computational biology.
His Machine learning research includes elements of Representation, Expert system and Knowledge acquisition.
Fuzzy logic is a component of his Fuzzy set, Fuzzy set operations, Fuzzy classification and Fuzzy control system studies.
His study looks at the intersection of Fuzzy classification and topics like Defuzzification with Neuro-fuzzy and Fuzzy associative matrix.
He most often published in these fields:
- Artificial intelligence (32.73%)
- Data mining (29.50%)
- Machine learning (17.27%)
What were the highlights of his more recent work (between 2014-2020)?
- Artificial intelligence (32.73%)
- Data mining (29.50%)
- Machine learning (17.27%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Data mining, Machine learning, Cluster analysis and Computational biology.
His biological study deals with issues like Pattern recognition, which deal with fields such as Protein sequencing.
His work carried out in the field of Data mining brings together such families of science as Fuzzy set, Statistical classification, Set and Time series.
His study on Artificial neural network is often connected to Set as part of broader study in Machine learning.
His Cluster analysis study combines topics from a wide range of disciplines, such as Node, Network topology, Theoretical computer science and Pairwise comparison.
Keith C. C. Chan has included themes like Quantitative trait locus, microRNA, Gene and Similarity in his Computational biology study.
Between 2014 and 2020, his most popular works were:
- Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding (85 citations)
- Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest. (81 citations)
- ILNCSIM: improved lncRNA functional similarity calculation model (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of investigation include Artificial intelligence, Data mining, Protein sequencing, Cluster analysis and Computational biology.
His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Chen and Scale and other disciplines.
His studies deal with areas such as Sentiment analysis and Social media, Social media mining as well as Data mining.
His Protein sequencing study incorporates themes from Classifier, Support vector machine, Pattern recognition and Protein–protein interaction.
His Cluster analysis research incorporates elements of Disjoint sets, Algorithm design, Fuzzy logic and Power graph analysis.
The study incorporates disciplines such as Cross-validation, Transcription, microRNA, Gene and Bipartite graph in addition to Computational biology.
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