What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Data mining, Machine learning, Artificial neural network and Pattern recognition.
Artificial intelligence is a component of his Classifier, Support vector machine, Cluster analysis, Decision tree and Feature selection studies.
As part of the same scientific family, he usually focuses on Data mining, concentrating on Self-organizing map and intersecting with Fuzzy logic, MovieLens, Recommender system and Collaborative filtering.
Chih-Fong Tsai does research in Machine learning, focusing on Ensemble learning specifically.
His Artificial neural network research includes elements of Bankruptcy prediction and Training set.
His biological study deals with issues like Data pre-processing, which deal with fields such as Data set, Undersampling, Data point and Decision tree learning.
His most cited work include:
- Review: Intrusion detection by machine learning: A review (567 citations)
- Using neural network ensembles for bankruptcy prediction and credit scoring (352 citations)
- CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (265 citations)
What are the main themes of his work throughout his whole career to date?
Chih-Fong Tsai mainly focuses on Artificial intelligence, Data mining, Machine learning, Pattern recognition and Support vector machine.
Artificial intelligence connects with themes related to Bankruptcy prediction in his study.
His work carried out in the field of Data mining brings together such families of science as Logistic regression, Categorical variable, Cluster analysis and Outlier.
His research in Machine learning tackles topics such as Training set which are related to areas like Fuzzy logic.
His Pattern recognition research incorporates elements of Contextual image classification, Feature, Computer vision and Image retrieval.
His Support vector machine research integrates issues from Anomaly detection and Curse of dimensionality.
He most often published in these fields:
- Artificial intelligence (59.17%)
- Data mining (42.50%)
- Machine learning (33.33%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (59.17%)
- Pattern recognition (30.00%)
- Data mining (42.50%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Data mining, Feature selection and Instance selection.
He regularly ties together related areas like Machine learning in his Artificial intelligence studies.
His Machine learning study incorporates themes from Bankruptcy prediction and Intelligent machine.
His Pattern recognition study combines topics from a wide range of disciplines, such as Decision tree learning, Genetic algorithm, Feature and Cluster analysis.
His study in Data mining is interdisciplinary in nature, drawing from both Classifier and Missing value imputation.
Chih-Fong Tsai combines subjects such as Ensemble learning, Boosting, Kernel and Ranking SVM with his study of Feature selection.
Between 2016 and 2021, his most popular works were:
- Clustering-based undersampling in class-imbalanced data (193 citations)
- SVM and SVM Ensembles in Breast Cancer Prediction. (96 citations)
- Under-sampling class imbalanced datasets by combining clustering analysis and instance selection (57 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Chih-Fong Tsai mainly investigates Artificial intelligence, Pattern recognition, Data mining, Support vector machine and Cluster analysis.
Specifically, his work in Artificial intelligence is concerned with the study of Kernel.
Chih-Fong Tsai has researched Pattern recognition in several fields, including Sampling, Boosting and Decision tree learning.
His Data mining study incorporates themes from Classifier, Bankruptcy prediction, Undersampling and Machine learning.
His work on Kernel as part of general Support vector machine research is often related to Breast cancer, thus linking different fields of science.
He has included themes like Instance selection, Statistical classification and Class in his Cluster analysis study.
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