Ingo Mierswa
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Co-authors
- Michael WurstRalf KlinkenbergMartin ScholzKatharina MorikSimon FischerAlfred UltschFabian MoerchenJohannes H. Schulte
- Topics
- Metaheuristic Optimization Algorithms Research (7 papers)Evolutionary Algorithms and Applications (6 papers)Advanced Multi-Objective Optimization Algorithms (4 papers)
In The Last Decade
Ingo Mierswa
20 papers receiving 906 citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Artificial Intelligence 481
- Computer Vision and Pattern Recognition 216
- Signal Processing 214
- Information Systems 204
- Computer Networks and Communications 104
Countries citing papers authored by Ingo Mierswa
This map shows the geographic impact of Ingo Mierswa'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 Ingo Mierswa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ingo Mierswa more than expected).
Fields of papers citing papers by Ingo Mierswa
This network shows the impact of papers produced by Ingo Mierswa. 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 Ingo Mierswa. The network helps show where Ingo Mierswa may publish in the future.
Co-authorship network of co-authors of Ingo Mierswa
This figure shows the co-authorship network connecting the top 25 collaborators of Ingo Mierswa. A scholar is included among the top collaborators of Ingo Mierswa 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 Ingo Mierswa. Ingo Mierswa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 9 | |
| 4 | 6 | |
| 5 | 1 | |
| 6 | Regularization through Multi-Objective Optimization. | 1 |
| 7 | 16 | |
| 8 | Sound Multi-objective Feature Space Transformation for Clustering | 3 |
| 9 | 30 | |
| 10 | 18 | |
| 11 | 5 | |
| 12 | 2 | |
| 13 | 0 | |
| 14 | 33 | |
| 15 | Efficient Feature Construction by Meta Learning - Guiding the Search in Meta Hypothesis Space | 3 |
| 16 | 91 | |
| 17 | 69 | |
| 18 | 2 | |
| 19 | Automatic Feature Extraction from Large Time Series. | 2 |
| 20 | A Flexible Platform for Knowledge Discovery Experiments: YALE - Yet Another Learning Environment | 8 |
About Ingo Mierswa
Ingo Mierswa is a scholar working on Signal Processing, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 25 papers that have together received 1.0k indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (7 papers), Evolutionary Algorithms and Applications (6 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). The work is most often cited by research in Signal Processing (214 citations), Artificial Intelligence (481 citations) and Computer Science Applications (62 citations). Ingo Mierswa has collaborated with scholars based in Germany and Greece. Frequent co-authors include Michael Wurst, Ralf Klinkenberg, Martin Scholz, Katharina Morik, Simon Fischer, Alfred Ultsch, Fabian Moerchen, Johannes H. Schulte, Sebastian Fischer and Lars Kaderali. Their work appears in journals such as Cancer Letters, Machine Learning and Advances in Data Analysis and Classification.
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.