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Computer Science

D-Index
56
Citations
13738
World Ranking
4041
National Ranking
25

Overview

Johannes Fürnkranz is affiliated with Johannes Kepler University of Linz in Austria. Their research primarily focuses on computer science, with a strong emphasis on artificial intelligence.

The scholar has a significant number of publications in the field of artificial intelligence, including work related to machine learning and data classification. Their expertise extends to areas such as text and document classification technologies, imbalanced data classification techniques, artificial intelligence in games, explainable artificial intelligence (XAI), Bayesian modeling and causal inference, and machine learning and algorithms.

Frequent publication venues for Johannes Fürnkranz include:

  • arXiv (Cornell University)
  • Machine Learning
  • Lecture Notes in Computer Science
  • 2021 IEEE Conference on Games (CoG)
  • Frontiers in Artificial Intelligence

Notable recent papers authored or co-authored by Fürnkranz are:

  • "A review of possible effects of cognitive biases on interpretation of rule-based machine learning models," 2021, Artificial Intelligence
  • "Comparing Boosting and Bagging for Decision Trees of Rankings," 2021, Journal of Classification
  • "Explainable and interpretable machine learning and data mining," 2024, Data Mining and Knowledge Discovery
  • "Frontiers in Artificial Intelligence / An empirical investigation into deep and shallow rule learning," 2021, University Library Linz repository (Johannes Kepler Universität Linz)
  • "Efficient learning of large sets of locally optimal classification rules," 2023, Machine Learning

Johannes Fürnkranz has worked with several frequent co-authors, including:

  • Eneldo Loza Mencía
  • Eyke Hüllermeier
  • Timo Bertram
  • Florian Beck
  • Moritz Kulessa

Best Publications

  • Multilabel classification via calibrated label ranking

    Johannes Fürnkranz;Eyke Hüllermeier;Eneldo Loza Mencía;Klaus Brinker

  • Preference Learning and Ranking by Pairwise Comparison

    Johannes Fürnkranz;Eyke Hüllermeier

  • Separate-and-Conquer Rule Learning

    Johannes Fürnkranz

  • Label ranking by learning pairwise preferences

    Eyke Hüllermeier;Johannes Fürnkranz;Weiwei Cheng;Klaus Brinker

  • Round robin classification

    Johannes Fürnkranz

  • Incremental reduced error pruning

    Johannes Fürnkranz;Gerhard Widmer

  • Large-scale multi-label text classification — revisiting neural networks

    Jinseok Nam;Jungi Kim;Eneldo Loza Mencía;Iryna Gurevych

  • A Study Using $n$-gram Features for Text Categorization

    Johannes Fürnkranz

  • ROC 'n' rule learning: towards a better understanding of covering algorithms

    Johannes Fürnkranz;Peter A. Flach

  • Deep Belief Networks

    Unknown

  • Preference Learning

    Unknown

  • Pairwise preference learning and ranking

    Johannes Fürnkranz;Eyke Hüllermeier

  • Knowledge Discovery in Databases: PKDD 2006

    Johannes Fürnkranz;Tobias Scheffer;Myra Spiliopoulou

  • Unsupervised generation of data mining features from linked open data

    Heiko Paulheim;Johannes Fümkranz

  • Efficient pairwise multilabel classification for large-scale problems in the legal domain

    Eneldo Loza Mencía;Johannes Fürnkranz

  • Exploiting Structural Information for Text Classification on the WWW

    Johannes Fürnkranz

  • An Evaluation of Grading Classifiers

    Alexander K. Seewald;Johannes Fürnkranz

  • Pruning Algorithms for Rule Learning

    Johannes Fürnkranz

  • A Unified Model for Multilabel Classification and Ranking

    Klaus Brinker;Johannes Fürnkranz;Eyke Hüllermeier

  • Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification

    Jinseok Nam;Eneldo Loza Mencía;Hyunwoo J. Kim;Johannes Fürnkranz

  • An analysis of rule evaluation metrics

    Johannes Fürnkranz;Peter A. Flach

  • A survey of preference-based reinforcement learning methods

    Christian Wirth;Riad Akrour;Gerhard Neumann;Johannes Fürnkranz

Frequent Co-Authors

Eyke Hüllermeier
Eyke Hüllermeier Ludwig-Maximilians-Universität München
Nada Lavrač
Nada Lavrač Jozef Stefan Institute
Myra Spiliopoulou
Myra Spiliopoulou Otto-von-Guericke University Magdeburg
Tobias Scheffer
Tobias Scheffer University of Potsdam
Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Peter A. Flach
Peter A. Flach University of Bristol
Heiko Paulheim
Heiko Paulheim University of Mannheim
Iryna Gurevych
Iryna Gurevych Technical University of Darmstadt
Francesco C. Billari
Francesco C. Billari Bocconi University
Thore Graepel
Thore Graepel University College London

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