Sašo Džeroski

19.4k total citations · 3 hit papers
327 papers, 9.4k citations indexed

About

Sašo Džeroski is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Sašo Džeroski has authored 327 papers receiving a total of 9.4k indexed citations (citations by other indexed papers that have themselves been cited), including 155 papers in Artificial Intelligence, 64 papers in Molecular Biology and 56 papers in Information Systems. Recurrent topics in Sašo Džeroski's work include Data Mining Algorithms and Applications (48 papers), Machine Learning and Data Classification (34 papers) and Rough Sets and Fuzzy Logic (27 papers). Sašo Džeroski is often cited by papers focused on Data Mining Algorithms and Applications (48 papers), Machine Learning and Data Classification (34 papers) and Rough Sets and Fuzzy Logic (27 papers). Sašo Džeroski collaborates with scholars based in Slovenia, Germany and Belgium. Sašo Džeroski's co-authors include Bernard Ženko, Dragi Kocev, Ljupčo Todorovski, Nada Lavrač, Luc De Raedt, Jan Struyf, Celine Vens, Hendrik Blockeel, Stefan Wrobel and Gjorgji Madjarov and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Sašo Džeroski

315 papers receiving 8.8k citations

Hit Papers

Is Combining Classifiers with Stacking Better than Select... 2004 2026 2011 2018 2004 2012 2005 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Is Combining Classifiers with Stacking Better than Selecting the Best One?
    2004 595 citations Sašo Džeroski et al. Machine Learning profile →
  2. An extensive experimental comparison of methods for multi-label learning
    2012 497 citations Dragi Kocev, Sašo Džeroski et al. profile →
  3. Proceedings of the 22nd international conference on Machine learning
    2005 430 citations Sašo Džeroski et al. profile →

Peers

Sašo Džeroski
Michael Steinbach United States
Louis Wehenkel Belgium
Tin Kam Ho United States
J. S. Marron United States
Carla E. Brodley United States
Sotiris Kotsiantis Greece
José A. Lozano Spain
Thomas G. Dietterich United States
Geoffrey I. Webb Australia
Joydeep Ghosh United States
Michael Steinbach United States
Sašo Džeroski
Citations per year, relative to Sašo Džeroski Sašo Džeroski (= 1×) peers Michael Steinbach

Countries citing papers authored by Sašo Džeroski

Since Specialization
Citations

This map shows the geographic impact of Sašo Džeroski's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sašo Džeroski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sašo Džeroski more than expected).

Fields of papers citing papers by Sašo Džeroski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sašo Džeroski. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sašo Džeroski. The network helps show where Sašo Džeroski may publish in the future.

Co-authorship network of co-authors of Sašo Džeroski

This figure shows the co-authorship network connecting the top 25 collaborators of Sašo Džeroski. A scholar is included among the top collaborators of Sašo Džeroski based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sašo Džeroski. Sašo Džeroski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Doerr, Carola, et al.. (2025). Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 487–490.
3.
Kocev, Dragi, et al.. (2024). Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–16. 8 indexed citations
4.
Mrvič, Tatjana, Bojana Beovič, Mateja Logar, et al.. (2024). The Impact of COVID-19 on Multidrug-Resistant Bacteria at a Slovenian Tertiary Medical Center. Antibiotics. 13(3). 214–214. 2 indexed citations
5.
Zalar, Polona, Cene Gostinčar, Sašo Džeroski, et al.. (2023). Xerophilic fungi contaminating historically valuable easel paintings from Slovenia. Frontiers in Microbiology. 14. 1258670–1258670. 6 indexed citations
6.
Migh, Ede, István Grexa, Attila G. Végh, et al.. (2023). Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era. Biosensors. 13(2). 187–187. 4 indexed citations
7.
Džeroski, Sašo, et al.. (2023). Dimensionally-consistent equation discovery through probabilistic attribute grammars. Information Sciences. 632. 742–756. 5 indexed citations
8.
Mihailović, D., et al.. (2023). Boosting the performance of quantum annealers using machine learning. Quantum Machine Intelligence. 5(1). 2 indexed citations
9.
Vermetten, Diederick, et al.. (2022). The importance of landscape features for performance prediction of modular CMA-ES variants. Proceedings of the Genetic and Evolutionary Computation Conference. 648–656. 5 indexed citations
10.
Kosjek, Tina, et al.. (2022). Machine learning for identification of silylated derivatives from mass spectra. Journal of Cheminformatics. 14(1). 62–62. 5 indexed citations
11.
12.
Džeroski, Sašo, et al.. (2022). Determining Exception Context in Assembly Operations from Multimodal Data. Sensors. 22(20). 7962–7962. 2 indexed citations
13.
Tanevski, Jovan, et al.. (2022). Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations. Machine Learning. 112(4). 1337–1363. 1 indexed citations
14.
Doerr, Carola, et al.. (2022). Explainable Model-specific Algorithm Selection for Multi-Label Classification. SPIRE - Sciences Po Institutional REpository. 39–46. 2 indexed citations
15.
Panov, Panče, et al.. (2020). Incremental predictive clustering trees for online semi-supervised multi-target regression. Machine Learning. 109(11). 2121–2139. 6 indexed citations
16.
Tanevski, Jovan, et al.. (2019). Meta-Model Framework for Surrogate-Based Parameter Estimation in Dynamical Systems. IEEE Access. 7. 181829–181841. 6 indexed citations
17.
Zalar, Polona, et al.. (2015). Halophily reloaded: new insights into the extremophilic life-style of Wallemia with the description of Wallemia hederae sp. nov. Fungal Diversity. 76(1). 97–118. 35 indexed citations
18.
Dimitrovski, Ivica, Dragi Kocev, Suzana Loškovska, & Sašo Džeroski. (2010). Detection of Visual Concepts and Annotation of Images Using Predictive Clustering Trees.. The Plant Cell. 23(2). 823–42. 5 indexed citations
19.
Džeroski, Sašo, et al.. (1998). Diterpene structure elucidation from 13cnmr spectra with inductive logic programming. Applied Artificial Intelligence. 12(5). 363–383. 18 indexed citations
20.
Džeroski, Sašo, et al.. (1995). Knowledge discovery in a water quality database. Knowledge Discovery and Data Mining. 28(2). 81–86. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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