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Hendrik Blockeel

Hendrik Blockeel

D-Index & Metrics

Computer Science

D-Index
51
Citations
12018
World Ranking
5284
National Ranking
57

Overview

Hendrik Blockeel is affiliated with KU Leuven in Belgium and has contributed extensively to the field of computer science, with a focus on artificial intelligence and related areas. Their research encompasses a range of subfields including artificial intelligence, signal processing, electrical and electronic engineering, information systems, and computer vision and pattern recognition.

Their recent publications include:

  • Decision trees: from efficient prediction to responsible AI, 2023, Frontiers in Artificial Intelligence
  • A Scalable Ensemble Approach to Forecast the Electricity Consumption of Households, 2022, IEEE Transactions on Smart Grid
  • Scenario generation of residential electricity consumption through sampling of historical data, 2023, Sustainable Energy Grids and Networks
  • Feature Interactions in XGBoost, 2020, arXiv (Cornell University)
  • DeepSaDe: Learning Neural Networks That Guarantee Domain Constraint Satisfaction, 2024, Proceedings of the AAAI Conference on Artificial Intelligence

Their frequent collaborators include:

  • Wannes Meert
  • Aras Yurtman
  • Jonas Soenen
  • Kshitij Goyal
  • Daan Van Wesenbeeck

Common publication venues for Hendrik's work are:

  • arXiv (Cornell University)
  • Data Mining and Knowledge Discovery
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Frontiers in Artificial Intelligence
  • IEEE Transactions on Smart Grid

The main areas of study they have contributed to include computer science broadly, with notable work in artificial intelligence comprising 43 publications, signal processing with 15 publications, and additional contributions in electrical and electronic engineering, information systems, and computer vision and pattern recognition.

Their research topics cover:

  • Time Series Analysis and Forecasting
  • Machine Learning and Data Classification
  • Anomaly Detection Techniques and Applications
  • Text and Document Classification Technologies
  • Music and Audio Processing
  • Bayesian Modeling and Causal Inference
  • Data Mining Algorithms and Applications

Best Publications

  • Web mining research: a survey

    Raymond Kosala;Hendrik Blockeel

  • Top-down induction of first-order logical decision trees

    Hendrik Blockeel;Luc De Raedt

  • Decision trees for hierarchical multi-label classification

    Celine Vens;Jan Struyf;Leander Schietgat;Sašo Džeroski

  • Top-Down Induction of Clustering Trees

    Hendrik Blockeel;Luc De Raedt;Jan Ramon

  • Relational Reinforcement Learning

    Saso Dzeroski;Luc De Raedt;Hendrik Blockeel

  • Predicting gene function using hierarchical multi-label decision tree ensembles

    Leander Schietgat;Celine Vens;Jan Struyf;Hendrik Blockeel

  • Knowledge Discovery in Databases: PKDD 2003

    Nada Lavrač;Dragan Gamberger;Ljupčo Todorovski;Hendrik Blockeel

  • Efficient algorithms for decision tree cross-validation

    Hendrik Blockeel;Jan Struyf

  • Decision trees for hierarchical multilabel classification: a case study in functional genomics

    Hendrik Blockeel;Leander Schietgat;Jan Struyf;Sašo Džeroski

  • Improving the efficiency of inductive logic programming through the use of query packs

    Hendrik Blockeel;Luc Dehaspe;Bart Demoen;Gerda Janssens

  • Scaling Up Inductive Logic Programming by Learning from Interpretations

    Hendrik Blockeel;Luc De Raedt;Nico Jacobs;Bart Demoen

  • Decision trees: from efficient prediction to responsible AI

    Unknown

  • Machine Learning: ECML 2003

    Nada Lavrač;Dragan Gamberger;Hendrik Blockeel;Ljupčo Todorovski

  • Hierarchical multi-classification

    Hendrik Blockeel;Maurice Bruynooghe;Sašo Džeroski;Jan Ramon

  • Experiment databases

    Joaquin Vanschoren;Hendrik Blockeel;Bernhard Pfahringer;Geoffrey Holmes

  • Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner

    Kurt Driessens;Jan Ramon;Hendrik Blockeel

  • Mining data from intensive care patients

    Jan Ramon;Daan Fierens;Fabián Güiza;Geert Meyfroidt

  • Multi-instance tree learning

    Hendrik Blockeel;David Page;Ashwin Srinivasan

  • Query transformations for improving the efficiency of ilp systems

    Vítor Santos Costa;Ashwin Srinivasan;Rui Camacho;Hendrik Blockeel

  • On estimating model accuracy with repeated cross-validation

    Gitte Vanwinckelen;Hendrik Blockeel

  • Top-down induction of logical decision trees

    Hendrik Blockeel;Luc De Raedt

  • Multi-relational data mining

    Arno Knobbe;H. Blockeel;Arno Siebes;van der D.M.G. Wallen

  • Top-down induction of first order logical decision trees

    Hendrik Blockeel

  • Decision trees for hierarchical multilabel classification: A case study in functional genomics

    Leander Schietgat;Hendrik Blockeel;Jan Struyf;Saso Dzeroski

Frequent Co-Authors

Jan Ramon
Jan Ramon French Institute for Research in Computer Science and Automation - INRIA
Luc De Raedt
Luc De Raedt KU Leuven
Joaquin Vanschoren
Joaquin Vanschoren Eindhoven University of Technology
Sašo Džeroski
Sašo Džeroski Jožef Stefan Institute
Andreas Bender
Andreas Bender University of Cambridge
Siegfried Nijssen
Siegfried Nijssen Université Catholique de Louvain
Bart Goethals
Bart Goethals University of Antwerp
Toon Calders
Toon Calders University of Antwerp
Kristian Kersting
Kristian Kersting Technical University of Darmstadt

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