Tomas Horvath
tomas horvath

Dr Tomas Horvath

  

Biography

Dr. Tomáš Horváth is a professor at the Edinburgh Napier University since 2024. He received his MSc and PhD degrees at the Pavol Jozef Šafárik University in Košice, Slovakia, in 2002 and 2008, respectively, in the area of relational learning. Since 2004, he is a the member of the faculty of the Institute of Computer Science of the Faculty of Science at this university. He was on a post-doc internship at the Information Systems and Machine Learning lab of the University in Hildesheim, Germany, from 2009 to 2012 where he was working in the area of recommender systems with applications in education. From 2015 to 2016 he received a post-doc grant at the Department of Computer Science, University of São Paulo in São Carlos, Brazil, where he started to work on automated machine learning with focus on hyper-parameter learning. From 2016 until 2024, he was working as an associate professor at the Faculty of Informatics at the Eötvös Loránd University in Budapest, Hungary where he built the Department of Data Science and Engineering of which he was the head of for more than 6 years. He was continuing to work on automated working on automated machine learning and started to work on applied machine learning for the agriculture domain. From 2024 he is a professor of artificial intelligence at the Edinburgh Napier University, Scotland, UK.

His research interests include relational and rule-based learning, pattern mining, recommender systems, automated machine learning and precision farming.

School

Research Groups

Themes

Esteem

Advisory panels and expert committees or witness

  • Steering Committee Member of the European Conference on Advances in Databases and Information Systems
  • Steering committee member of the Conference Information technologies -- Applications and Theory

 

Editorial Activity

  • Associate Editor of the International Journal of Intelligent Data Analysis

 

Fellowships and Awards

  • Best Paper Award at the 22nd International Conference on Intelligent Data Engineering and Automated Learning, Manchester, UK
  • Best Student Paper Award of the 11thInternational Conference on Computer Supported Education, Noordwijkerhout, The Netherlands
  • Best Student Paper Award of the 3rd International Conference on Computer Supported Education, Noordwijkerhout, The Netherlands

 

Invited Speaker

  • Invited lecture on the 3rd Seminar on Digital Agriculture, Osijek, Croatia
  • Invited lecture on the 4th Seminar on Digital Agriculture, Osijek, Croatia
  • Invited lecture at the Summer School on Autonomous Driving at University of Maribor, Slovenia
  • Invited talk at the conference Dáta a Znalosti & WIKT, Košice, Slovak Republik
  • Invited talk at the Machine Learning Meetup in, Košice, Slovakia
  • Tutorial at the conference Znalosti, Mikulov, Czech Republic
  • Invited talk at Charles University in Prague, Czech Republic
  • Invited lecture at University of Malaga, Spain

 

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Date


36 results

Cluster-based oversampling with area extraction from representative points for class imbalance learning

Journal Article
Farou, Z., Wang, Y., & Horváth, T. (2024)
Cluster-based oversampling with area extraction from representative points for class imbalance learning. Intelligent Systems with Applications, 22, Article 200357. https://doi.org/10.1016/j.iswa.2024.200357
Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to a...

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms

Journal Article
Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number o...

A Comparative Study of Assessment Metrics for Imbalanced Learning

Conference Proceeding
Farou, Z., Aharrat, M., & Horváth, T. (2023)
A Comparative Study of Assessment Metrics for Imbalanced Learning. In New Trends in Database and Information Systems: ADBIS 2023 Short Papers, Doctoral Consortium and Workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings (119-129). https://doi.org/10.1007/978-3-031-42941-5_11
There are several machine learning algorithms addressing class imbalance problem, requiring standardized metrics for adequete performance evaluation. This paper reviews severa...

Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices

Conference Proceeding
Antoni, L., Eliaš, P., Horváth, T., Krajči, S., Krídlo, O., & Török, C. (2023)
Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices. In Graph-Based Representation and Reasoning: 28th International Conference on Conceptual Structures, ICCS 2023, Berlin, Germany, September 11–13, 2023, Proceedings (19-27). https://doi.org/10.1007/978-3-031-40960-8_2
Formal Concept Analysis identifies hidden patterns in data that can be presented to the user or the data analyst. We propose a method for analyzing the correlation matrices ba...

NCC: Neural concept compression for multilingual document recommendation

Journal Article
Tashu, T. M., Lenz, M., & Horváth, T. (2023)
NCC: Neural concept compression for multilingual document recommendation. Applied Soft Computing, 142, Article 110348. https://doi.org/10.1016/j.asoc.2023.110348
In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to i...

Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features

Journal Article
Horváth, T., Mantovani, R. G., & de Carvalho, A. C. (2023)
Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features. Applied Soft Computing, 134, Article 109969. https://doi.org/10.1016/j.asoc.2022.109969
Meta-learning, a concept from the area of automated machine learning, aims at providing decision support for data scientists by recommending a suitable setting (a machine lear...

Solving Multi-class Imbalance Problems Using Improved Tabular GANs

Conference Proceeding
Farou, Z., Kopeikina, L., & Horváth, T. (2022)
Solving Multi-class Imbalance Problems Using Improved Tabular GANs. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (527-539). https://doi.org/10.1007/978-3-031-21753-1_51
Multi-class imbalance problems are non-standard derivative data science problems. These problems are associated with the skewness in the data underlying distribution, which, i...

Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring

Conference Proceeding
Tashu, T. M., & Horváth, T. (2022)
Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (12-21). https://doi.org/10.1007/978-3-031-21753-1_2
Automatic essay scoring (AES) models based on neural networks (NN) have had a lot of success. However, research has shown that NN-based AES models have robustness issues, such...

Object Detection Using Sim2Real Domain Randomization for Robotic Applications

Journal Article
Horváth, D., Erdős, G., Istenes, Z., Horváth, T., & Földi, S. (2023)
Object Detection Using Sim2Real Domain Randomization for Robotic Applications. IEEE Transactions on Robotics, 39(2), 1225-1243. https://doi.org/10.1109/tro.2022.3207619
Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the fiel...

Dynamic noise filtering for multi-class classification of beehive audio data

Journal Article
Várkonyi, D. T., Seixas Junior, J. L., & Horváth, T. (2023)
Dynamic noise filtering for multi-class classification of beehive audio data. Expert Systems with Applications, 213(Part A), Article 118850. https://doi.org/10.1016/j.eswa.2022.118850
Honeybees are the most specialized insect pollinators and are critical not only for honey production but, also, for keeping the environmental balance by pollinating the flower...

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