World's Best Scientists 2026 revealed!
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Computer Science
Slovenia
2026

D-Index & Metrics

Computer Science

D-Index
57
Citations
15925
World Ranking
3780
National Ranking
2

Research.com Recognitions

  • 2026 - Research.com Computer Science in Slovenia Leader Award
  • 2025 - Research.com Computer Science in Slovenia Leader Award
  • 2023 - Research.com Computer Science in Slovenia Leader Award
  • 2022 - Research.com Computer Science in Slovenia Leader Award

Overview

Nada Lavrač is affiliated with the Jozef Stefan Institute in Slovenia. Their research spans multiple fields, with a significant focus on computer science and biochemistry, genetics, and molecular biology. Their publication record reflects a diverse engagement with topics across these disciplines.

The main fields of study associated with Nada Lavrač include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

Their work extends into several subfields, notably:

  • Artificial Intelligence
  • Molecular Biology
  • Plant Science
  • Statistical and Nonlinear Physics
  • Computational Theory and Mathematics

Key research topics covered by their publications include:

  • Topic Modeling
  • Biomedical Text Mining and Ontologies
  • Advanced Text Analysis Techniques
  • Natural Language Processing Techniques
  • Bioinformatics and Genomic Networks
  • Machine Learning and Data Classification
  • Semantic Web and Ontologies

Nada Lavrač has published extensively in prominent venues, with frequent publications appearing in:

  • arXiv (Cornell University)
  • Machine Learning
  • Zenodo (CERN European Organization for Nuclear Research)
  • Machine Learning and Knowledge Extraction
  • IEEE Access

Notable papers authored or co-authored by Nada Lavrač include:

  • Advancing manufacturing systems with big-data analytics: A conceptual framework (2020) - International Journal of Computer Integrated Manufacturing
  • LemmaGen: Multilingual Lemmatisation with Induced Ripple-Down Rules (2020) - TUGraz OPEN Library (Graz University of Technology)
  • tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification (2020) - Computer Speech & Language
  • Feature Importance Estimation with Self-Attention Networks (2020) - arXiv (Cornell University)
  • autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021) - Machine Learning

Frequent collaborators include:

  • Blaž Škrlj
  • Senja Pollak
  • Boshko Koloski
  • Bojan Cestnik
  • Matej Martinc

Best Publications

  • The multi-purpose incremental learning system AQ15 and its testing application to three medical domains

    Ryszard S. Michalski;Igor Mozetic;Jiarong Hong;Nada Lavrac

  • Inductive Logic Programming: Techniques and Applications

    Nada Lavrac;Saso Dzeroski

  • Relational Data Mining

    Saso Dzeroski;Nada Lavrac

  • Rule Evaluation Measures: A Unifying View

    Nada Lavrac;Peter A. Flach;Blaz Zupan

  • Foundations of Rule Learning

    Johannes Frnkranz;Dragan Gamberger;Nada Lavrac

  • Selected Techniques for Data Mining in Medicine

    Nada Lavrač

  • Subgroup Discovery with CN2-SD

    Nada Lavrač;Branko Kavšek;Peter Flach;Ljupčo Todorovski

  • Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

    Petra Kralj Novak;Nada Lavrač;Geoffrey I. Webb

  • Propositionalization approaches to relational data mining

    Stefan Kramer;Nada Lavrač;Peter Flach

  • Stream-based active learning for sentiment analysis in the financial domain

    Jasmina Smailović;Miha Grčar;Nada Lavrač;Martin Žnidaršič

  • Learning nonrecursive definitions of relations with LINUS

    Nada Lavrač;Sašo Džeroski;Marko Grobelnik

  • Expert-guided subgroup discovery: methodology and application

    Dragan Gamberger;Nada Lavrac

  • APRIORI-SD: ADAPTING ASSOCIATION RULE LEARNING TO SUBGROUP DISCOVERY

    Branko Kavšek;Nada Lavrač;Viktor Jovanoski

  • Kardio : A Study in Deep and Qualitative Knowledge for Expert Systems

    Ivan Bratko;Igor Mozetič;Nada Lavrač

  • The AQ15 Inductive Learning System: An Overview and Experiments

    Ryszard S. Michalski;Igor Mozetic;Jiarong Hong;Nada Lavrac

  • Experiments with Noise Filtering in a Medical Domain

    Dragan Gamberger;Nada Lavrac;Ciril Groselj

  • A Rule based Approach to Word Lemmatization

    Joël Plisson;Nada Lavrac;Dunja Mladenic

  • Knowledge Discovery in Databases: PKDD 2003

    Nada Lavrač;Dragan Gamberger;Ljupčo Todorovski;Hendrik Blockeel

  • Comparative Evaluation of Approaches to Propositionalization

    Mark-A. Krogel;Simon Rawles;Filip Železný;Filip Železný;Peter A. Flach

  • Noise detection and elimination in data preprocessing: Experiments in medical domains

    Dragan Gamberger;Nada Lavrac;Saso Dzeroski

Frequent Co-Authors

Peter A. Flach
Peter A. Flach University of Bristol
Johannes Fürnkranz
Johannes Fürnkranz Johannes Kepler University of Linz
Sašo Džeroski
Sašo Džeroski Jožef Stefan Institute
Dunja Mladenic
Dunja Mladenic Jožef Stefan Institute
Hannu Toivonen
Hannu Toivonen University of Helsinki
Jakub Tolar
Jakub Tolar University of Minnesota
Stefan Wrobel
Stefan Wrobel University of Bonn
Geraint A. Wiggins
Geraint A. Wiggins Vrije Universiteit Brussel
Ivan Bratko
Ivan Bratko University of Ljubljana
Luc De Raedt
Luc De Raedt KU Leuven

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