H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 56 Citations 11,573 287 World Ranking 2007 National Ranking 90

Overview

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, Machine learning, Fuzzy logic, Data mining and Fuzzy set. The Artificial intelligence study combines topics in areas such as Theoretical computer science, Preference, Preference learning, Extension and Pattern recognition. His research on Machine learning often connects related topics like Pairwise comparison.

His Fuzzy logic research is multidisciplinary, incorporating elements of Association rule learning, Mathematical optimization and Probability distribution. His Data mining study integrates concerns from other disciplines, such as Similitude and Cluster analysis. The concepts of his Fuzzy set study are interwoven with issues in Probabilistic risk assessment, Statistics, Probability theory and Fuzzy control system.

His most cited work include:

  • Multilabel classification via calibrated label ranking (544 citations)
  • Label ranking by learning pairwise preferences (396 citations)
  • An Approach to Modelling and Simulation of Uncertain Dynamical Systems (291 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, Data mining, Fuzzy logic and Preference learning. His research in Artificial intelligence is mostly concerned with Pairwise comparison. In most of his Machine learning studies, his work intersects topics such as Extension.

His studies in Data stream mining and Association rule learning are all subfields of Data mining research. His Preference learning study is related to the wider topic of Preference. In his study, which falls under the umbrella issue of Fuzzy classification, Defuzzification and Fuzzy number is strongly linked to Fuzzy set operations.

He most often published in these fields:

  • Artificial intelligence (57.58%)
  • Machine learning (40.79%)
  • Data mining (15.38%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (57.58%)
  • Machine learning (40.79%)
  • Multi-label classification (5.59%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Machine learning, Multi-label classification, Training set and Benchmark. His studies deal with areas such as Context, Preference learning, Natural language processing, Ranking and Key as well as Artificial intelligence. In his research on the topic of Preference learning, Learning to rank is strongly related with Analogy.

His work carried out in the field of Ranking brings together such families of science as Generalization, Pairwise comparison, Kernel and Graph. He works mostly in the field of Machine learning, limiting it down to topics relating to Classifier and, in certain cases, Expected utility hypothesis. His research investigates the link between Multi-label classification and topics such as Rule-based system that cross with problems in Conformity.

Between 2018 and 2021, his most popular works were:

  • Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction. (32 citations)
  • Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods (28 citations)
  • Multi-target prediction: a unifying view on problems and methods (22 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

Eyke Hüllermeier spends much of his time researching Artificial intelligence, Machine learning, Aleatoric music, Uncertainty quantification and Relevance. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Context, Preference learning and Extension. His Preference learning research is multidisciplinary, relying on both Learning to rank and Analogy.

Eyke Hüllermeier interconnects Key and Regression in the investigation of issues within Machine learning. His biological study spans a wide range of topics, including Data modeling, Systems design and Fuzzy set, Fuzzy logic. His Generalization research integrates issues from Ensemble learning and Pairwise comparison.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Multilabel classification via calibrated label ranking

Johannes Fürnkranz;Eyke Hüllermeier;Eneldo Loza Mencía;Klaus Brinker.
Machine Learning (2008)

830 Citations

Preference Learning and Ranking by Pairwise Comparison

Johannes Fürnkranz;Eyke Hüllermeier.
Preference Learning (2010)

668 Citations

Label ranking by learning pairwise preferences

Eyke Hüllermeier;Johannes Fürnkranz;Weiwei Cheng;Klaus Brinker.
Artificial Intelligence (2008)

609 Citations

An Approach to Modelling and Simulation of Uncertain Dynamical Systems

Eyke Hüllermeier.
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (1997)

459 Citations

FURIA: an algorithm for unordered fuzzy rule induction

Jens Christian Hühn;Eyke Hüllermeier.
Data Mining and Knowledge Discovery (2009)

416 Citations

On label dependence and loss minimization in multi-label classification

Krzysztof Dembczyński;Willem Waegeman;Weiwei Cheng;Eyke Hüllermeier.
Machine Learning (2012)

331 Citations

Online clustering of parallel data streams

Jürgen Beringer;Eyke Hüllermeier.
data and knowledge engineering (2006)

329 Citations

Combining instance-based learning and logistic regression for multilabel classification

Weiwei Cheng;Eyke Hüllermeier.
european conference on machine learning (2009)

318 Citations

Open challenges for data stream mining research

Georg Krempl;Indre Žliobaite;Dariusz Brzeziński;Eyke Hüllermeier.
Sigkdd Explorations (2014)

275 Citations

Pairwise preference learning and ranking

Johannes Fürnkranz;Eyke Hüllermeier.
european conference on machine learning (2003)

266 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

Contact us

Top Scientists Citing Eyke Hüllermeier

Francisco Herrera

Francisco Herrera

University of Granada

Publications: 85

Henri Prade

Henri Prade

Paul Sabatier University

Publications: 85

Humberto Bustince

Humberto Bustince

Universidad Publica De Navarra

Publications: 75

Didier Dubois

Didier Dubois

Centre national de la recherche scientifique, CNRS

Publications: 51

Edwin Lughofer

Edwin Lughofer

Johannes Kepler University of Linz

Publications: 49

Benjamin Bedregal

Benjamin Bedregal

Federal University of Rio Grande do Norte

Publications: 47

Johannes Fürnkranz

Johannes Fürnkranz

Johannes Kepler University of Linz

Publications: 40

Radko Mesiar

Radko Mesiar

Slovak University of Technology in Bratislava

Publications: 32

Witold Pedrycz

Witold Pedrycz

University of Alberta

Publications: 31

Bernard De Baets

Bernard De Baets

Ghent University

Publications: 25

Gleb Beliakov

Gleb Beliakov

Deakin University

Publications: 25

Alberto Fernández

Alberto Fernández

University of Granada

Publications: 23

Grigorios Tsoumakas

Grigorios Tsoumakas

Aristotle University of Thessaloniki

Publications: 23

João Gama

João Gama

University of Porto

Publications: 23

Min-Ling Zhang

Min-Ling Zhang

Southeast University

Publications: 22

Something went wrong. Please try again later.