What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
Jerzy Stefanowski focuses on Data mining, Artificial intelligence, Machine learning, Rough set and Minority class.
As a member of one scientific family, Jerzy Stefanowski mostly works in the field of Data mining, focusing on Classifier and, on occasion, Concept drift.
The concepts of his Artificial intelligence study are interwoven with issues in Data analysis and Pattern recognition.
In his study, Outlier and Ensemble learning is strongly linked to Open research, which falls under the umbrella field of Machine learning.
His work deals with themes such as Pancreatitis, Acute pancreatitis, Decision rule and Extension, which intersect with Rough set.
His studies deal with areas such as Algorithm and Complete information as well as Dominance-based rough set approach.
His most cited work include:
- Ensemble learning for data stream analysis (411 citations)
- Incomplete Information Tables and Rough Classification (303 citations)
- Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (241 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Data mining, Rough set and Imbalanced data.
Artificial intelligence connects with themes related to Pattern recognition in his study.
Jerzy Stefanowski combines topics linked to Component with his work on Machine learning.
His study on Data stream mining is often connected to Sensitivity as part of broader study in Data mining.
The Dominance-based rough set approach research Jerzy Stefanowski does as part of his general Rough set study is frequently linked to other disciplines of science, such as Generalization, therefore creating a link between diverse domains of science.
He has included themes like Decision tree and Resampling in his Imbalanced data study.
He most often published in these fields:
- Artificial intelligence (66.91%)
- Machine learning (51.80%)
- Data mining (47.48%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (66.91%)
- Machine learning (51.80%)
- Imbalanced data (25.90%)
In recent papers he was focusing on the following fields of study:
Jerzy Stefanowski mainly focuses on Artificial intelligence, Machine learning, Imbalanced data, Data mining and Class imbalance.
His Artificial intelligence study frequently draws connections to adjacent fields such as Pattern recognition.
His biological study focuses on Minority class.
Decomposition and Identification is closely connected to Open research in his research, which is encompassed under the umbrella topic of Data mining.
His Class imbalance research is multidisciplinary, incorporating elements of Random subspace method and Noisy data.
His Ensemble learning study incorporates themes from Subspace topology and Rough set.
Between 2013 and 2021, his most popular works were:
- Ensemble learning for data stream analysis (411 citations)
- Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm (228 citations)
- SMOTE-IPF (209 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Data mining
Jerzy Stefanowski mostly deals with Data mining, Artificial intelligence, Machine learning, Data stream mining and Class imbalance.
His research investigates the link between Data mining and topics such as Classifier that cross with problems in Statistical classification.
His Machine learning research includes elements of Class and Open research.
His research in Data stream mining focuses on subjects like Complex data type, which are connected to Knowledge extraction and Full cycle.
His Class imbalance research integrates issues from Noise reduction and Noisy data.
His Concept drift research is multidisciplinary, relying on both Tree structure, Measure and Pattern recognition.
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