World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
5999
World Ranking
11199
National Ranking
202

Overview

Joaquin Vanschoren is affiliated with Eindhoven University of Technology in the Netherlands. Their primary field of study is computer science, with a focus on artificial intelligence and related subfields.

Their research activity spans various subfields including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Radiology, Nuclear Medicine and Imaging
  • Information Systems and Management

Joaquin Vanschoren's work covers multiple topics, particularly within machine learning and data analysis:

  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Algorithms
  • Data Stream Mining Techniques
  • Anomaly Detection Techniques and Applications
  • Imbalanced Data Classification Techniques
  • COVID-19 diagnosis using AI

The scientist has published extensively in several venues, with a notable frequency in the following publication sources:

  • arXiv (Cornell University)
  • Machine Learning
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Sensors
  • Proceedings of the Genetic and Evolutionary Computation Conference Companion

Some of their recent papers include:

  • Advances and Challenges in Meta-Learning: A Technical Review, 2024, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Importance of Tuning Hyperparameters of Machine Learning Algorithms, 2020, arXiv (Cornell University)
  • Meta-features for meta-learning, 2022, Knowledge-Based Systems
  • Adaptation Strategies for Automated Machine Learning on Evolving Data, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • DataPerf: Benchmarks for Data-Centric AI Development, 2022, arXiv (Cornell University)

Their frequent co-authors include:

  • Jan N. van Rijn
  • Carlos Soares
  • Pavel Brazdil
  • Murat Onur Yildirim
  • Elif Ceren Gok Yildirim

Joaquin Vanschoren is also an author of books published by Springer Nature, notably a book titled Metalearning published in 2022.

Best Publications

  • OpenML: networked science in machine learning

    Joaquin Vanschoren;Jan N. van Rijn;Bernd Bischl;Luis Torgo

  • Automated Machine Learning

    Frank Hutter;Lars Kotthoff;Joaquin Vanschoren

  • Meta-Learning: A Survey

    Joaquin Vanschoren

  • ASlib: A Benchmark Library for Algorithm Selection

    Bernd Bischl;Pascal Kerschke;Lars Kotthoff;Marius Thomas Lindauer

  • Advances and Challenges in Meta-Learning: A Technical Review

    Unknown

  • An Open Source AutoML Benchmark

    Pieter Gijsbers;Erin LeDell;Janek Thomas;Sébastien Poirier

  • OpenML: A collaborative science platform

    Jan van Rijn;Bernd Bischl;Luis Torgo;Bo Gao

  • Effectiveness of Random Search in SVM hyper-parameter tuning

    Rafael G. Mantovani;Andre L. D. Rossi;Joaquin Vanschoren;Bernd Bischl

  • A survey of intelligent assistants for data analysis

    Floarea Serban;Joaquin Vanschoren;Jörg-Uwe Kietz;Abraham Bernstein

  • The online performance estimation framework: heterogeneous ensemble learning for data streams

    Jan N. van Rijn;Jan N. van Rijn;Geoffrey Holmes;Bernhard Pfahringer;Joaquin Vanschoren

  • Experiment databases

    Joaquin Vanschoren;Hendrik Blockeel;Bernhard Pfahringer;Geoffrey Holmes

  • Selecting classification algorithms with active testing

    Rui Leite;Pavel Brazdil;Joaquin Vanschoren

  • Meta-features for meta-learning

    Unknown

  • Hyper-Parameter Tuning of a Decision Tree Induction Algorithm

    Rafael G. Mantovani;Tomas Horvath;Ricardo Cerri;Joaquin Vanschoren

  • Importance of Tuning Hyperparameters of Machine Learning Algorithms.

    Hilde J. P. Weerts;Andreas C. Mueller;Joaquin Vanschoren

  • Fast Algorithm Selection Using Learning Curves

    Jan N. van Rijn;Salisu Mamman Abdulrahman;Pavel Brazdil;Joaquin Vanschoren

  • DataPerf: Benchmarks for Data-Centric AI Development

    Unknown

  • Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning

    Hendrik Blockeel;Joaquin Vanschoren

  • Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

    Iván Olier;Iván Olier;Noureddin Sadawi;Noureddin Sadawi;G. Richard J. Bickerton;Joaquin Vanschoren

  • Adaptation Strategies for Automated Machine Learning on Evolving Data

    Bilge Celik;Joaquin Vanschoren

  • Algorithm selection on data streams

    Jan N. van Rijn;Geoffrey Holmes;Bernhard Pfahringer;Joaquin Vanschoren

  • Data augmentation using conditional generative adversarial networks for leaf counting in arabidopsis plants

    Yezi Zhu;Marc Aoun;Marcel P.C.M. Krijn;J. Vanschoren

  • OpenML Benchmarking Suites and the OpenML100.

    Bernd Bischl;Giuseppe Casalicchio;Matthias Feurer;Frank Hutter

  • OpenML-Python: an extensible Python API for OpenML

    Matthias Feurer;Jan N. van Rijn;Jan N. van Rijn;Arlind Kadra;Pieter Gijsbers

Frequent Co-Authors

Bernd Bischl
Bernd Bischl Ludwig-Maximilians-Universität München
Geoffrey Holmes
Geoffrey Holmes University of Waikato
Frank Hutter
Frank Hutter University of Freiburg
Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
André C. P. L. F. de Carvalho
André C. P. L. F. de Carvalho Universidade de São Paulo
Ross D. King
Ross D. King University of Manchester
Crina Grosan
Crina Grosan King's College London
Holger H. Hoos
Holger H. Hoos RWTH Aachen University
Daniel Lakens
Daniel Lakens Eindhoven University of Technology

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