What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Sašo Džeroski mainly focuses on Artificial intelligence, Machine learning, Decision tree, Inductive logic programming and Random forest.
He mostly deals with Knowledge extraction in his studies of Artificial intelligence.
His Machine learning research is multidisciplinary, incorporating elements of Tree, Set, Gene and Pattern recognition.
Decision tree is a subfield of Data mining that he tackles.
The study incorporates disciplines such as Relational database, Theoretical computer science, Logic programming and Inductive programming in addition to Inductive logic programming.
His Random forest study integrates concerns from other disciplines, such as Interpretation, Proper linear model, Regression, Ensemble learning and Simple.
His most cited work include:
- A large-scale evaluation of computational protein function prediction (624 citations)
- Is Combining Classifiers with Stacking Better than Selecting the Best One (505 citations)
- Decision trees for hierarchical multi-label classification (427 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Cluster analysis and Decision tree.
His Artificial intelligence research incorporates elements of Tree and Pattern recognition.
The concepts of his Machine learning study are interwoven with issues in Classifier, Hierarchy and Regression.
His research in Data mining intersects with topics in Structure, Set and Benchmark.
His biological study spans a wide range of topics, including Feature ranking and Automatic image annotation.
His Inductive logic programming study incorporates themes from Inductive programming, Logic programming and Statistical relational learning.
He most often published in these fields:
- Artificial intelligence (44.70%)
- Machine learning (32.58%)
- Data mining (26.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (44.70%)
- Machine learning (32.58%)
- Cluster analysis (19.70%)
In recent papers he was focusing on the following fields of study:
Sašo Džeroski mostly deals with Artificial intelligence, Machine learning, Cluster analysis, Feature ranking and Pattern recognition.
His Artificial intelligence study integrates concerns from other disciplines, such as Tree and Extension.
His research integrates issues of Classifier, Free parameter and Regression in his study of Machine learning.
He focuses mostly in the field of Regression, narrowing it down to matters related to Supervised learning and, in some cases, Set.
His studies in Cluster analysis integrate themes in fields like Transfer of learning, Gene expression, Gene regulatory network and Overfitting.
His work investigates the relationship between Feature ranking and topics such as Benchmark that intersect with problems in Synthetic data and Data mining.
Between 2018 and 2021, his most popular works were:
- Analysis of 1.2 million foot scans from North America, Europe and Asia. (9 citations)
- Using machine learning to estimate herbage production and nutrient uptake on Irish dairy farms (7 citations)
- Predictive model for the quantitative analysis of human skin using photothermal radiometry and diffuse reflectance spectroscopy. (4 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Sašo Džeroski focuses on Artificial intelligence, Machine learning, Artificial neural network, Set and Foot.
His Artificial intelligence research includes elements of Tree and Pattern recognition.
His Decision tree learning study, which is part of a larger body of work in Machine learning, is frequently linked to Subject-matter expert, bridging the gap between disciplines.
His work deals with themes such as Random forest, Mutual information and Feature, which intersect with Artificial neural network.
The Set study combines topics in areas such as Annotation, Relation, Cheminformatics and Identification.
His study of Foot brings together topics like Heel, Statistics, 3d scanning, Grading and Single group.
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