Tuve Löfström
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 10%
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Statistics and Probability top 10%
- Co-authors
- Ulf JohanssonHenrik BoströmHenrik LinussonLars NiklassonRikard KönigRachid OucheikhUlf NorinderErnst Ahlberg
- Topics
- Machine Learning and Data Classification (27 papers)Neural Networks and Applications (21 papers)Imbalanced Data Classification Techniques (10 papers)
- Journals
- SHILAP Revista de lepidopterologíaExpert Systems with ApplicationsPattern Recognition
- Partner nations
- SwedenUnited KingdomGermany
In The Last Decade
Tuve Löfström
50 papers receiving 424 citations
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 284
- Computational Theory and Mathematics 51
- Control and Systems Engineering 49
- Computer Vision and Pattern Recognition 48
- Statistics and Probability 42
Countries citing papers authored by Tuve Löfström
This map shows the geographic impact of Tuve Löfström'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 Tuve Löfström with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tuve Löfström more than expected).
Fields of papers citing papers by Tuve Löfström
This network shows the impact of papers produced by Tuve Löfström. 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 Tuve Löfström. The network helps show where Tuve Löfström may publish in the future.
Co-authorship network of co-authors of Tuve Löfström
This figure shows the co-authorship network connecting the top 25 collaborators of Tuve Löfström. A scholar is included among the top collaborators of Tuve Löfström 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 Tuve Löfström. Tuve Löfström is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 11 | |
| 7 | 12 | |
| 8 | 2 | |
| 9 | On Effectively Creating Ensembles of Classifiers : Studies on Creation Strategies, Diversity and Predicting with Confidence | 5 |
| 10 | 8 | |
| 11 | 0 | |
| 12 | 2 | |
| 13 | 11 | |
| 14 | 4 | |
| 15 | 1 | |
| 16 | 7 | |
| 17 | Introducing GEMS * a Novel Technique for Ensemble Creation | 3 |
| 18 | Why Not Use an Oracle When You Got One | 10 |
| 19 | 8 | |
| 20 | Predicting the Benefit of Rule Extraction: A Novel Component in Data Mining | 3 |
About Tuve Löfström
Tuve Löfström is a scholar working on Artificial Intelligence, Statistics and Probability and Computational Theory and Mathematics, having authored 56 papers that have together received 467 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (27 papers), Neural Networks and Applications (21 papers) and Imbalanced Data Classification Techniques (10 papers). The work is most often cited by research in Artificial Intelligence (284 citations), Statistics and Probability (42 citations) and Transportation (32 citations). Tuve Löfström has collaborated with scholars based in Sweden, United Kingdom and Germany. Frequent co-authors include Ulf Johansson, Henrik Boström, Henrik Linusson, Lars Niklasson, Rikard König, Rachid Oucheikh, Ulf Norinder, Ernst Ahlberg, Lars Carlsson and R. König. Their work appears in journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Pattern Recognition.
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.