Niklas Lavesson

1.2k citations
61 papers · 680 · h-index 16

Impact in

    • Advanced Malware Detection Techniques
    • Anomaly Detection Techniques and Applications
    • Machine Learning and Data Classification
    • Data Stream Mining Techniques

Papers in

    • Machine Learning and Data Classification 15
    • Data Stream Mining Techniques 7
    • Imbalanced Data Classification Techniques 6
    • Spam and Phishing Detection 10
    • Data Mining Algorithms and Applications 7

Niklas Lavesson

59 papers receiving 628 citations

Peers

Niklas Lavesson
Comparison fields: 5 of 97
  • Signal Processing 133
  • Artificial Intelligence 266
  • Computer Vision and Pattern Recognition 136
  • Information Systems 146
  • Software 23
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Citations per field
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Citations per year

Countries citing papers authored by Niklas Lavesson

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Niklas Lavesson Line = papers co-authored together Niklas Lavesson links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 61 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201755
2 202050
3 201347
4
Quantifying the impact of learning algorithm parameter tuning
200635
5 201132
6
Model Based Decision Support for Value and Sustainability in Product Development
201531
7 201828
8
Comparative Analysis of Voting Schemes for Ensemble-based Malware Detection
201326
9 201026
10 201425
11 201023
12 201021
13 202221
14 201116
15 201115
16 201515
17 201415
18 200714
19 200813
20 201612

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 61 papers that have together received 680 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (15 papers), Advanced Malware Detection Techniques (10 papers), Spam and Phishing Detection (10 papers), Network Security and Intrusion Detection (7 papers), Data Stream Mining Techniques (7 papers), Data Mining Algorithms and Applications (7 papers), Time Series Analysis and Forecasting (6 papers) and Imbalanced Data Classification Techniques (6 papers). The work is most often cited by research in Signal Processing (133 citations), Artificial Intelligence (266 citations), Computer Vision and Pattern Recognition (136 citations), Information Systems (146 citations) and Software (23 citations). Niklas Lavesson has collaborated with scholars based in Sweden, Bulgaria and United States. Frequent co-authors include Paul Davidsson, Håkan Grahn, Henric Johnson, Florian Westphal, Martin Boldt, Johan Hall, Amir Yavariabdi, Veselka Boeva, Christian Johansson and Dirk Vanhoudt. Their work appears in journals such as Expert Systems with Applications, Knowledge and Information Systems, International Journal on Document Analysis and Recognition (IJDAR), Automated Software Engineering and Empirical Software Engineering.

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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