Niklas Lavesson
Impact in
- Signal Processing top 5%
- Advanced Malware Detection Techniques
- Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
- Data Stream Mining Techniques
Papers in
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- Machine Learning and Data Classification 21
- Data Stream Mining Techniques 11
- Anomaly Detection Techniques and Applications 7
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- Spam and Phishing Detection 11
- Data Mining Algorithms and Applications 8
- Co-authors
- Paul Davidsson (14 shared papers)Håkan Grahn (17 shared papers)Henric Johnson (7 shared papers)Veselka Boeva (13 shared papers)Florian Westphal (7 shared papers)Eva García-Martín (6 shared papers)Martin Boldt (6 shared papers)Amir Yavariabdi (1 shared paper)
In The Last Decade
Niklas Lavesson
76 papers receiving 867 citations
Peers
Comparison fields: 5 of 106
- Signal Processing 176
- Artificial Intelligence 389
- Information Systems 203
- Computer Vision and Pattern Recognition 181
- Software 33
Countries citing papers authored by Niklas Lavesson
This map shows the geographic impact of Niklas Lavesson'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 Niklas Lavesson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niklas Lavesson more than expected).
Fields of papers citing papers by Niklas Lavesson
This network shows the impact of papers produced by Niklas Lavesson. 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 Niklas Lavesson. The network helps show where Niklas Lavesson may publish in the future.
Co-authors
The 25 scholars most cited alongside Niklas Lavesson, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 78 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 59 | |
| 2 | 2020 | 58 | |
| 3 | 2013 | 52 | |
| 4 | Quantifying the impact of learning algorithm parameter tuning | 2006 | 42 |
| 5 | 2011 | 39 | |
| 6 | 2018 | 37 | |
| 7 | 2011 | 35 | |
| 8 | 2010 | 34 | |
| 9 | Model Based Decision Support for Value and Sustainability in Product Development | 2015 | 32 |
| 10 | Comparative Analysis of Voting Schemes for Ensemble-based Malware Detection | 2013 | 29 |
| 11 | 2014 | 28 | |
| 12 | 2022 | 24 | |
| 13 | 2010 | 24 | |
| 14 | 2010 | 22 | |
| 15 | 2011 | 20 | |
| 16 | 2011 | 19 | |
| 17 | 2007 | 18 | |
| 18 | 2017 | 18 | |
| 19 | 2019 | 17 | |
| 20 | 2014 | 15 |
About Niklas Lavesson
Niklas Lavesson is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 78 papers that have together received 929 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (21 papers), Advanced Malware Detection Techniques (12 papers), Spam and Phishing Detection (11 papers), Data Stream Mining Techniques (11 papers), Data Mining Algorithms and Applications (8 papers), Network Security and Intrusion Detection (8 papers), Anomaly Detection Techniques and Applications (7 papers) and Time Series Analysis and Forecasting (7 papers). The work is most often cited by research in Signal Processing (176 citations), Artificial Intelligence (389 citations), Information Systems (203 citations), Computer Vision and Pattern Recognition (181 citations) and Software (33 citations). Niklas Lavesson has collaborated with scholars based in Sweden, Bulgaria and Germany. Frequent co-authors include Paul Davidsson, Håkan Grahn, Henric Johnson, Veselka Boeva, Florian Westphal, Eva García-Martín, Martin Boldt, Amir Yavariabdi, Johan Hall and Andreas Jacobsson. Their work appears in journals such as Expert Systems with Applications, Knowledge and Information Systems, Lecture notes in computer science, Social Network Analysis and Mining and Sensors.
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.