Ulf Johansson

1.8k total citations
121 papers, 1.1k citations indexed

About

Ulf Johansson is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, Ulf Johansson has authored 121 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Artificial Intelligence, 22 papers in Information Systems and 16 papers in Computational Theory and Mathematics. Recurrent topics in Ulf Johansson's work include Machine Learning and Data Classification (35 papers), Neural Networks and Applications (30 papers) and Data Mining Algorithms and Applications (22 papers). Ulf Johansson is often cited by papers focused on Machine Learning and Data Classification (35 papers), Neural Networks and Applications (30 papers) and Data Mining Algorithms and Applications (22 papers). Ulf Johansson collaborates with scholars based in Sweden, United Kingdom and Iran. Ulf Johansson's co-authors include Tuve Löfström, Henrik Boström, Lars Niklasson, Rikard König, Henrik Linusson, Harry Eriksson, Ulf Norinder, Andres Kiviste, Jan Stenlid and Urban Nilsson and has published in prestigious journals such as Expert Systems with Applications, Frontiers in Plant Science and Pattern Recognition.

In The Last Decade

Ulf Johansson

110 papers receiving 955 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ulf Johansson Sweden 17 553 133 132 102 86 121 1.1k
Bob McKay South Korea 20 1.1k 1.9× 236 1.8× 154 1.2× 34 0.3× 280 3.3× 92 1.5k
Shuangyin Liu China 20 301 0.5× 26 0.2× 145 1.1× 80 0.8× 42 0.5× 100 1.7k
Gonzalo Martínez-Muñoz Spain 20 756 1.4× 65 0.5× 115 0.9× 16 0.2× 63 0.7× 41 1.2k
Jayanta Kumar Basak India 21 597 1.1× 72 0.5× 140 1.1× 12 0.1× 93 1.1× 119 1.8k
Wei Lu China 21 618 1.1× 221 1.7× 97 0.7× 16 0.2× 25 0.3× 135 1.3k
Md. Nasir Sulaiman Malaysia 19 513 0.9× 69 0.5× 341 2.6× 7 0.1× 67 0.8× 138 1.5k
Liangmin Wang China 27 1.0k 1.9× 29 0.2× 781 5.9× 53 0.5× 30 0.3× 173 2.8k
Nicolás García‐Pedrajas Spain 28 1.7k 3.0× 293 2.2× 214 1.6× 10 0.1× 157 1.8× 93 2.4k
Shaoning Pang New Zealand 18 820 1.5× 79 0.6× 202 1.5× 6 0.1× 105 1.2× 73 1.7k
Jiaxu Ning China 11 367 0.7× 126 0.9× 46 0.3× 25 0.2× 32 0.4× 30 741

Countries citing papers authored by Ulf Johansson

Since Specialization
Citations

This map shows the geographic impact of Ulf Johansson'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 Ulf Johansson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ulf Johansson more than expected).

Fields of papers citing papers by Ulf Johansson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ulf Johansson. 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 Ulf Johansson. The network helps show where Ulf Johansson may publish in the future.

Co-authorship network of co-authors of Ulf Johansson

This figure shows the co-authorship network connecting the top 25 collaborators of Ulf Johansson. A scholar is included among the top collaborators of Ulf Johansson 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 Ulf Johansson. Ulf Johansson 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.
Löfström, Tuve, et al.. (2025). Classification with reject option: Distribution-free error guarantees via conformal prediction. Machine Learning with Applications. 20. 100664–100664.
2.
Löfström, Tuve, et al.. (2023). Investigating the impact of calibration on the quality of explanations. Annals of Mathematics and Artificial Intelligence. 2 indexed citations
3.
Derba‐Maceluch, Marta, Madhavi Latha Gandla, Pramod Sivan, et al.. (2023). Field testing of transgenic aspen from large greenhouse screening identifies unexpected winners. Plant Biotechnology Journal. 21(5). 1005–1021. 6 indexed citations
4.
Derba‐Maceluch, Marta, Pramod Sivan, Madhavi Latha Gandla, et al.. (2023). Impact of xylan on field productivity and wood saccharification properties in aspen. Frontiers in Plant Science. 14. 1218302–1218302. 6 indexed citations
5.
Fahlvik, Nils & Ulf Johansson. (2021). Growth of northern red oak in southern Sweden. Scandinavian Journal of Forest Research. 36(6). 442–447. 3 indexed citations
6.
Derba‐Maceluch, Marta, Fariba Amini, Prashant Mohan‐Anupama Pawar, et al.. (2020). Cell Wall Acetylation in Hybrid Aspen Affects Field Performance, Foliar Phenolic Composition and Resistance to Biological Stress Factors in a Construct-Dependent Fashion. Frontiers in Plant Science. 11. 651–651. 10 indexed citations
7.
Johansson, Ulf, et al.. (2011). The Trade-Off between Accuracy and Interpretability for Predictive In Silico Modeling. Future Medicinal Chemistry. 3. 1 indexed citations
8.
Johansson, Ulf, Rikard König, & Lars Niklasson. (2008). Evolving a Locally Optimized Instance Based Learner. Borås Academic Digital Archive (University of Borås). 124–129. 5 indexed citations
9.
Johansson, Ulf, et al.. (2008). Genetic Decision Lists for Concept Description. Borås Academic Digital Archive (University of Borås). 450–456. 1 indexed citations
10.
König, Rikard, Ulf Johansson, & Lars Niklasson. (2008). Using Genetic Programming to Increase Rule Quality. Borås Academic Digital Archive (University of Borås). 288–293. 5 indexed citations
11.
Johansson, Ulf, Henrik Boström, & Rikard König. (2008). Extending Nearest Neighbor Classification with Spheres of Confidence. KTH Publication Database DiVA (KTH Royal Institute of Technology). 282–287. 6 indexed citations
12.
Löfström, Tuve, Ulf Johansson, & Henrik Boström. (2008). On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers. KTH Publication Database DiVA (KTH Royal Institute of Technology). 127–132. 11 indexed citations
13.
Johansson, Ulf, et al.. (2006). Rule Extraction from Opaque Models-- A Slightly Different Perspective. 22–27. 7 indexed citations
14.
Johansson, Ulf, Tuve Löfström, Rikard König, & Lars Niklasson. (2006). Introducing GEMS * a Novel Technique for Ensemble Creation. The Florida AI Research Society. 700–705. 3 indexed citations
15.
Johansson, Ulf, Tuve Löfström, Rikard König, & Lars Niklasson. (2006). Why Not Use an Oracle When You Got One. International Conference on Neural Information Processing. 10. 227–236. 10 indexed citations
16.
Löfström, Tuve & Ulf Johansson. (2005). Predicting the Benefit of Rule Extraction: A Novel Component in Data Mining. 7(3). 3 indexed citations
17.
Johansson, Ulf, et al.. (2004). Why Rule Extraction Matters. International Conference on Software Engineering. 47–52. 5 indexed citations
18.
Johansson, Ulf, Rikard König, & Lars Niklasson. (2004). The Truth is in There : Rule Extraction from Opaque Models Using Genetic Programming. The Florida AI Research Society. 658–663. 33 indexed citations
19.
Johansson, Ulf, Lars Niklasson, & Rikard König. (2004). Accuracy vs. comprehensibility in data mining models. International Conference on Information Fusion. 295–300. 28 indexed citations
20.
Niklasson, Lars, Henrik Engström, & Ulf Johansson. (2001). An Adaptive 'Rock, Scissors and Paper' Player Based on a Tapped Delay Neural Network. KTH Publication Database DiVA (KTH Royal Institute of Technology). 130–136. 4 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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