H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 42 Citations 6,458 189 World Ranking 4085 National Ranking 33

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Edwin Lughofer mostly deals with Artificial intelligence, Machine learning, Fuzzy control system, Fuzzy logic and Data mining. His work on Artificial neural network, Training set and Semantics as part of his general Artificial intelligence study is frequently connected to USable, thereby bridging the divide between different branches of science. His biological study spans a wide range of topics, including Classifier and System identification.

His Fuzzy control system course of study focuses on Interpretability and Anomaly detection and Visual inspection. His Fuzzy logic research includes elements of Contextual image classification, Nonlinear system and Pattern recognition. Edwin Lughofer studies Data stream mining which is a part of Data mining.

His most cited work include:

  • FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models (256 citations)
  • Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications (232 citations)
  • PANFIS: A Novel Incremental Learning Machine (190 citations)

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

His main research concerns Artificial intelligence, Machine learning, Fuzzy control system, Fuzzy logic and Data mining. His Artificial intelligence study combines topics from a wide range of disciplines, such as Context, Data stream mining and Pattern recognition. His study in the field of Active learning and Incremental learning also crosses realms of Active learning and Field.

The concepts of his Fuzzy control system study are interwoven with issues in Fuzzy set, Interpretability, Mathematical optimization and Curse of dimensionality. His studies in Fuzzy logic integrate themes in fields like Algorithm, Robustness and System identification. As a member of one scientific family, Edwin Lughofer mostly works in the field of Data mining, focusing on Cluster analysis and, on occasion, Vector quantization.

He most often published in these fields:

  • Artificial intelligence (52.72%)
  • Machine learning (37.66%)
  • Fuzzy control system (29.29%)

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

  • Artificial intelligence (52.72%)
  • Artificial neural network (13.39%)
  • Fuzzy control system (29.29%)

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

Edwin Lughofer mainly investigates Artificial intelligence, Artificial neural network, Fuzzy control system, Data stream mining and Machine learning. His Artificial intelligence research includes themes of Flexibility, Data science and Pattern recognition. His study in Artificial neural network is interdisciplinary in nature, drawing from both Nonlinear system, Trajectory, Fuzzy logic and Big data.

His work on Fuzzy set as part of general Fuzzy logic study is frequently linked to Data modeling and Steering wheel, bridging the gap between disciplines. His research in Fuzzy control system intersects with topics in Data stream, Algorithm and Pruning. When carried out as part of a general Data stream mining research project, his work on Concept drift is frequently linked to work in Scatternet, therefore connecting diverse disciplines of study.

Between 2019 and 2021, his most popular works were:

  • DEVDAN: Deep evolving denoising autoencoder (25 citations)
  • Online Tool Condition Monitoring Based on Parsimonious Ensemble (20 citations)
  • PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles (13 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Artificial neural network, Condition monitoring, Pattern recognition and Regression analysis. His studies in Artificial intelligence integrate themes in fields like Data stream mining, Concept drift and Reduction. The Artificial neural network study combines topics in areas such as Control system, Control theory, Sliding mode control, PID controller and Big data.

His Condition monitoring research incorporates themes from Physical model, Wiener process and Robustness. His Pattern recognition study combines topics in areas such as Data stream, Denoising autoencoder and Flexibility. Edwin Lughofer interconnects Transfer of learning, Data mining, Specific-information and Domain knowledge in the investigation of issues within Regression analysis.

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.

Top Publications

Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications

Edwin Lughofer.
(2011)

429 Citations

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

E.D. Lughofer.
IEEE Transactions on Fuzzy Systems (2008)

335 Citations

PANFIS: A Novel Incremental Learning Machine

Mahardhika Pratama;Sreenatha G. Anavatti;Plamen P. Angelov;Edwin Lughofer.
IEEE Transactions on Neural Networks (2014)

222 Citations

Evolving fuzzy classifiers using different model architectures

P. Angelov;E. Lughofer;X. Zhou.
Fuzzy Sets and Systems (2008)

207 Citations

Extensions of vector quantization for incremental clustering

Edwin Lughofer.
Pattern Recognition (2008)

176 Citations

Handling drifts and shifts in on-line data streams with evolving fuzzy systems

E. Lughofer;P. Angelov.
soft computing (2011)

170 Citations

Learning in Non-Stationary Environments: Methods and Applications

Moamar Sayed-Mouchaweh;Edwin Lughofer.
(2012)

167 Citations

GENEFIS: Toward an Effective Localist Network

Mahardhika Pratama;Sreenatha G. Anavatti;Edwin Lughofer.
IEEE Transactions on Fuzzy Systems (2014)

145 Citations

Generalized smart evolving fuzzy systems

Edwin Lughofer;Carlos Cernuda;Stefan Kindermann;Mahardhika Pratama.
Evolving Systems (2015)

142 Citations

Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

Werner Zellinger;Thomas Grubinger;Edwin Lughofer;Thomas Natschläger.
arXiv: Machine Learning (2017)

134 Citations

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

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