What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His scientific interests lie mostly in Artificial intelligence, Machine learning, Pairwise comparison, Preference learning and Classifier.
His Artificial intelligence research integrates issues from Ranking and Pattern recognition.
His research integrates issues of Algorithm and Data mining in his study of Machine learning.
The Classifier study combines topics in areas such as Boosting and Computation.
His studies in Instance-based learning integrate themes in fields like Learning classifier system and Algorithmic learning theory.
His studies deal with areas such as Binary classification and Spearman's rank correlation coefficient as well as Ranking SVM.
His most cited work include:
- Multilabel classification via calibrated label ranking (544 citations)
- Separate-and-Conquer Rule Learning (469 citations)
- Label ranking by learning pairwise preferences (396 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pairwise comparison, Heuristics and Data mining.
He brings together Artificial intelligence and Preference learning to produce work in his papers.
His study in Machine learning concentrates on Pruning, Ranking, Instance-based learning, Active learning and Ranking SVM.
His Boosting research extends to the thematically linked field of Pairwise comparison.
His Heuristics research incorporates themes from Consistency and Heuristic.
Association rule learning, Data stream mining and Decision tree are among the areas of Data mining where he concentrates his study.
He most often published in these fields:
- Artificial intelligence (66.90%)
- Machine learning (51.72%)
- Pairwise comparison (13.10%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (66.90%)
- Machine learning (51.72%)
- Interpretability (3.10%)
In recent papers he was focusing on the following fields of study:
Johannes Fürnkranz spends much of his time researching Artificial intelligence, Machine learning, Interpretability, Multi-label classification and Rule-based system.
His Artificial intelligence study frequently draws connections to other fields, such as Natural language processing.
His work blends Machine learning and Measure studies together.
His Multi-label classification research incorporates elements of Gradient boosting, Boosting, Conformity and Alternating decision tree.
His study in Rule-based system is interdisciplinary in nature, drawing from both Cognitive psychology, Discriminative model and Personalization.
His study looks at the relationship between Reinforcement learning and fields such as Tree, as well as how they intersect with chemical problems.
Between 2017 and 2021, his most popular works were:
- On cognitive preferences and the plausibility of rule-based models (19 citations)
- Which Scores to Predict in Sentence Regression for Text Summarization (12 citations)
- Batchwise Patching of Classifiers (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Artificial intelligence, Machine learning, Interpretability, Rule-based system and Natural language processing.
His Artificial intelligence research is multidisciplinary, incorporating elements of Field and Personalization.
His Machine learning study frequently draws connections between related disciplines such as Space.
His Rule-based system research incorporates elements of Debiasing, Position paper and Cognitive bias.
His research integrates issues of Context, Inference and Analogy in his study of Natural language processing.
His Artificial neural network research is multidisciplinary, relying on both Classifier, Pattern recognition, Concept drift and Transfer of learning.
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