D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 30 Citations 4,630 127 World Ranking 7032 National Ranking 264

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Bernd Bischl spends much of his time researching Artificial intelligence, Machine learning, Data mining, Benchmark and Optimization problem. His work often combines Artificial intelligence and Citizen science studies. His work on Hyperparameter and Model selection is typically connected to Work and Structure as part of general Machine learning study, connecting several disciplines of science.

His Hyperparameter study incorporates themes from Hyperparameter optimization, Visualization and Cluster analysis. Bernd Bischl combines subjects such as Toolbox, Kriging and Multi-objective optimization with his study of Data mining. His research in Benchmark intersects with topics in Black box, Bayesian optimization, Categorical variable, Modular design and Feature selection.

His most cited work include:

  • OpenML: networked science in machine learning (475 citations)
  • mlr: machine learning in R (249 citations)
  • Exploratory landscape analysis (161 citations)

What are the main themes of his work throughout his whole career to date?

Bernd Bischl focuses on Artificial intelligence, Machine learning, R package, Feature selection and Data mining. His Artificial intelligence research includes themes of Set and Pattern recognition. His work carried out in the field of Machine learning brings together such families of science as Set and Benchmark.

Many of his R package research pursuits overlap with Computational science, Work, Black box, Download and Interface. His Feature selection study integrates concerns from other disciplines, such as Algorithm and Boosting. The various areas that Bernd Bischl examines in his Data mining study include Statistical classification and Clustering high-dimensional data.

He most often published in these fields:

  • Artificial intelligence (53.23%)
  • Machine learning (41.79%)
  • R package (20.90%)

What were the highlights of his more recent work (between 2019-2021)?

  • R package (20.90%)
  • Artificial intelligence (53.23%)
  • Machine learning (41.79%)

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

His primary scientific interests are in R package, Artificial intelligence, Machine learning, Computational science and Feature selection. His research on Artificial intelligence frequently links to adjacent areas such as Survival analysis. As part of his studies on Machine learning, Bernd Bischl often connects relevant areas like Field.

His studies deal with areas such as Preprocessor and Computation as well as Computational science. His Benchmark research extends to Feature selection, which is thematically connected. His Benchmark course of study focuses on High dimensional and Data mining.

Between 2019 and 2021, his most popular works were:

  • Benchmark for filter methods for feature selection in high-dimensional classification data (63 citations)
  • Predicting personality from patterns of behavior collected with smartphones. (18 citations)
  • Multi-Objective Counterfactual Explanations (13 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Artificial intelligence, Machine learning, R package, Interpretability and Deep learning are his primary areas of study. His Artificial intelligence research is multidisciplinary, relying on both Documentation, Survival modeling, Survival analysis, License and Pattern recognition. Bernd Bischl does research in Machine learning, focusing on Categorical variable specifically.

His studies in Interpretability integrate themes in fields like Multi-objective optimization and Feature. His Multi-objective optimization research incorporates elements of Hyperparameter, Group method of data handling, Hyperparameter optimization, Benchmark and Evolutionary algorithm. His research investigates the link between Deep learning and topics such as Regression analysis that cross with problems in Mixture model, Regression and Deep neural networks.

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.

Best Publications

OpenML: networked science in machine learning

Joaquin Vanschoren;Jan N. van Rijn;Bernd Bischl;Luis Torgo.
Sigkdd Explorations (2014)

759 Citations

mlr: machine learning in R

Bernd Bischl;Michel Lang;Lars Kotthoff;Julia Schiffner.
Journal of Machine Learning Research (2016)

616 Citations

Exploratory landscape analysis

Olaf Mersmann;Bernd Bischl;Heike Trautmann;Mike Preuss.
genetic and evolutionary computation conference (2011)

240 Citations

ASlib: A Benchmark Library for Algorithm Selection

Bernd Bischl;Pascal Kerschke;Lars Kotthoff;Marius Thomas Lindauer.
Artificial Intelligence (2016)

157 Citations

Benchmark for filter methods for feature selection in high-dimensional classification data

Andrea Bommert;Xudong Sun;Bernd Bischl;Jörg Rahnenführer.
Computational Statistics & Data Analysis (2020)

156 Citations

Tunability: Importance of Hyperparameters of Machine Learning Algorithms

Philipp Probst;Anne-Laure Boulesteix;Bernd Bischl.
Journal of Machine Learning Research (2019)

156 Citations

Resampling methods for meta-model validation with recommendations for evolutionary computation

B. Bischl;O. Mersmann;H. Trautmann;C. Weihs.
Evolutionary Computation (2012)

153 Citations

Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

Bernd Bischl;Olaf Mersmann;Heike Trautmann;Mike Preuß.
genetic and evolutionary computation conference (2012)

143 Citations

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

Bernd Bischl;Jakob Richter;Jakob Bossek;Daniel Horn.
arXiv: Machine Learning (2017)

126 Citations

Personality traits predict smartphone usage.

Clemens Stachl;Sven Hilbert;Sven Hilbert;Jiew–Quay Au;Daniel Buschek.
European Journal of Personality (2017)

97 Citations

Best Scientists Citing Bernd Bischl

Frank Hutter

Frank Hutter

University of Freiburg

Publications: 43

Frank Neumann

Frank Neumann

University of Adelaide

Publications: 33

André C. P. L. F. de Carvalho

André C. P. L. F. de Carvalho

Universidade de São Paulo

Publications: 28

Eyke Hüllermeier

Eyke Hüllermeier

Ludwig-Maximilians-Universität München

Publications: 22

Thomas Bäck

Thomas Bäck

Leiden University

Publications: 18

Holger H. Hoos

Holger H. Hoos

Leiden University

Publications: 17

Jason H. Moore

Jason H. Moore

University of Pennsylvania

Publications: 15

Mike Preuss

Mike Preuss

Leiden University

Publications: 13

Mengjie Zhang

Mengjie Zhang

Victoria University of Wellington

Publications: 12

Bing Xue

Bing Xue

Victoria University of Wellington

Publications: 12

Thomas Stützle

Thomas Stützle

Université Libre de Bruxelles

Publications: 11

Andries P. Engelbrecht

Andries P. Engelbrecht

Stellenbosch University

Publications: 10

Jose A. Lozano

Jose A. Lozano

Basque Center for Applied Mathematics

Publications: 10

Francisco Herrera

Francisco Herrera

University of Granada

Publications: 9

José Hernández-Orallo

José Hernández-Orallo

Universitat Politècnica de València

Publications: 9

Christian Montag

Christian Montag

University of Ulm

Publications: 9

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

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