Marko Robnik‐Šikonja
- Artificial Intelligence top 0.5%
- Natural Language Processing Techniques 21
- Topic Modeling 16
- Text Readability and Simplification 8
- Explainable Artificial Intelligence (XAI) 7
- Machine Learning and Data Classification 6
- Neural Networks and Applications 5
- Health Informatics top 5%
- Signal Processing top 2%
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- Data Mining Algorithms and Applications 11
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- Rough Sets and Fuzzy Logic 5
- Co-authors
- Igor KononenkoErik ŠtrumbeljMirjana Kljajić BorštnarMarko BohanecSenja PollakNada LavračMatej MartincBlaž Škrlj
- Partner nations
- SloveniaGreeceUnited Kingdom
In The Last Decade
Marko Robnik‐Šikonja
78 papers receiving 4.0k citations
Hit Papers
Peers
Comparison fields: 5 of 196
- Artificial Intelligence 1.9k
- Computer Vision and Pattern Recognition 835
- Health Informatics 48
- Signal Processing 339
- Health Information Management 111
Countries citing papers authored by Marko Robnik‐Šikonja
This map shows the geographic impact of Marko Robnik‐Šikonja'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 Marko Robnik‐Šikonja with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marko Robnik‐Šikonja more than expected).
Fields of papers citing papers by Marko Robnik‐Šikonja
This network shows the impact of papers produced by Marko Robnik‐Šikonja. 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 Marko Robnik‐Šikonja. The network helps show where Marko Robnik‐Šikonja may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Marko Robnik‐Šikonja, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 3 | |
| 3 | To BAN or not to BAN | 2023 | 13 |
| 4 | 2023 | 5 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 11 | |
| 8 | 2022 | 14 | |
| 9 | Unsupervised Approach to Multilingual User Comments Summarization. | 2021 | 1 |
| 10 | NetSDM: Semantic Data Mining with Network Analysis | 2019 | 5 |
| 11 | 2019 | 2 | |
| 12 | 2018 | 2 | |
| 13 | 2017 | 3 | |
| 14 | 2015 | 2 | |
| 15 | 2013 | 36 | |
| 16 | 2003 | 24 | |
| 17 | Comprehensible Interpretation of Relief's Estimates | 2001 | 15 |
| 18 | Attribute Dependencies, Understandability and Split Selection in Tree Based Models | 1999 | 7 |
| 19 | Pruning Regression Trees with MDL. | 1998 | 16 |
| 20 | An adaptation of Relief for attribute estimation in regression | 1997 | 335 |
About Marko Robnik‐Šikonja
Marko Robnik‐Šikonja is a scholar working on Artificial Intelligence, Information Systems and Language and Linguistics, having authored 86 papers that have together received 4.2k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (21 papers), Topic Modeling (16 papers), Data Mining Algorithms and Applications (11 papers), Text Readability and Simplification (8 papers), Explainable Artificial Intelligence (XAI) (7 papers), Machine Learning and Data Classification (6 papers), Rough Sets and Fuzzy Logic (5 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Artificial Intelligence (1.9k citations), Computer Vision and Pattern Recognition (835 citations) and Health Informatics (48 citations). Marko Robnik‐Šikonja has collaborated with scholars based in Slovenia, Greece and United Kingdom. Frequent co-authors include Igor Kononenko, Erik Štrumbelj, Mirjana Kljajić Borštnar, Marko Bohanec, Senja Pollak, Nada Lavrač, Matej Martinc, Blaž Škrlj, D. Cukjati and Vid Podpečan.
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.