D-Index & Metrics Best Publications
Hendrik Blockeel

Hendrik Blockeel

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
Computer Science D-index 40 Citations 10,059 240 World Ranking 4548 National Ranking 48

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Programming language

Hendrik Blockeel mainly focuses on Artificial intelligence, Machine learning, Inductive logic programming, Decision tree and Statistical relational learning. His biological study spans a wide range of topics, including Tree and Data mining. Hendrik Blockeel combines subjects such as Gene expression profiling, Genome, Gene, Genomics and DNA microarray with his study of Machine learning.

His Inductive logic programming research is multidisciplinary, relying on both Theoretical computer science, Representation, Set, Cluster analysis and Discretization. His work carried out in the field of Decision tree brings together such families of science as Tilde and Top-down and bottom-up design. His research in Statistical relational learning focuses on subjects like Active learning, which are connected to Statistical hypothesis testing, Reusability, Generalizability theory and Repeatability.

His most cited work include:

  • Web mining research: a survey (1438 citations)
  • Top-down induction of first-order logical decision trees (564 citations)
  • Decision trees for hierarchical multi-label classification (427 citations)

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

Hendrik Blockeel focuses on Artificial intelligence, Machine learning, Data mining, Cluster analysis and Inductive logic programming. His research in Artificial intelligence intersects with topics in Relational database, Statistical relational learning and Pattern recognition. His studies deal with areas such as Tree, Context and Set as well as Machine learning.

His Data mining study frequently links to related topics such as Data science. His Inductive logic programming research incorporates elements of Theoretical computer science, Logic programming, Inductive programming and Natural language processing. As a part of the same scientific study, he usually deals with the Theoretical computer science, concentrating on Inference and frequently concerns with Algorithm.

He most often published in these fields:

  • Artificial intelligence (51.32%)
  • Machine learning (35.98%)
  • Data mining (21.16%)

What were the highlights of his more recent work (between 2013-2020)?

  • Artificial intelligence (51.32%)
  • Machine learning (35.98%)
  • Cluster analysis (15.34%)

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

Artificial intelligence, Machine learning, Cluster analysis, Data mining and Statistical relational learning are his primary areas of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Context, Relational database and Pattern recognition. The study incorporates disciplines such as Tree and Identification in addition to Machine learning.

His work in the fields of Correlation clustering overlaps with other areas such as Series. His research in the fields of Data stream mining overlaps with other disciplines such as Drug. His work investigates the relationship between Statistical relational learning and topics such as Feature learning that intersect with problems in Theoretical computer science, Autoencoder, Representation, Representation and Artificial neural network.

Between 2013 and 2020, his most popular works were:

  • Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3 (34 citations)
  • Using internal validity measures to compare clustering algorithms (27 citations)
  • Instance-level accuracy versus bag-level accuracy in multi-instance learning (18 citations)

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

  • Artificial intelligence
  • Machine learning
  • Programming language

The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Cluster analysis, Data mining and Statistical relational learning. Algorithmic learning theory and Inductive logic programming are among the areas of Artificial intelligence where the researcher is concentrating his efforts. His work on Canopy clustering algorithm, CURE data clustering algorithm and Constrained clustering as part of his general Machine learning study is frequently connected to Focus, thereby bridging the divide between different branches of science.

Hendrik Blockeel has included themes like Pairwise comparison and Constraint in his Cluster analysis study. His work is dedicated to discovering how Pairwise comparison, Construct are connected with Theoretical computer science and other disciplines. His studies in Data mining integrate themes in fields like Data science, Variety, Concept mining and Early results.

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

Web mining research: a survey

Raymond Kosala;Hendrik Blockeel.
Sigkdd Explorations (2000)

2446 Citations

Top-down induction of first-order logical decision trees

Hendrik Blockeel;Luc De Raedt.
Artificial Intelligence (1998)

988 Citations

Decision trees for hierarchical multi-label classification

Celine Vens;Jan Struyf;Leander Schietgat;Sašo Džeroski.
Machine Learning (2008)

627 Citations

Top-Down Induction of Clustering Trees

Hendrik Blockeel;Luc De Raedt;Jan Ramon.
international conference on machine learning (1998)

544 Citations

Relational Reinforcement Learning

Saso Dzeroski;Luc De Raedt;Hendrik Blockeel.
inductive logic programming (1998)

258 Citations

Knowledge Discovery in Databases: PKDD 2003

Nada Lavrač;Dragan Gamberger;Ljupčo Todorovski;Hendrik Blockeel.
(2003)

210 Citations

Predicting gene function using hierarchical multi-label decision tree ensembles

Leander Schietgat;Celine Vens;Jan Struyf;Hendrik Blockeel.
BMC Bioinformatics (2010)

189 Citations

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

Hendrik Blockeel;Leander Schietgat;Jan Struyf;Sašo Džeroski.
european conference on principles of data mining and knowledge discovery (2006)

181 Citations

Efficient algorithms for decision tree cross-validation

Hendrik Blockeel;Jan Struyf.
Journal of Machine Learning Research (2003)

173 Citations

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

Hendrik Blockeel;Luc Dehaspe;Bart Demoen;Gerda Janssens.
Journal of Artificial Intelligence Research (2002)

164 Citations

Editorial Boards

Machine Learning
(Impact Factor: 5.414)

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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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