[{"date_created":"2026-05-10T07:55:29Z","title":"Low Query Budget Active Learning for Classification and Regression","main_file_link":[{"url":"https://rdcu.be/fh03T","open_access":"1"}],"alternative_id":["6899"],"user_id":"226669","project":[{"_id":"f432a2ee-bceb-11ed-a251-a83585c5074d","name":"Institute for Data Science Solutions"},{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"}],"oa":"1","type":"conference","publication_identifier":{"eisbn":["978-3-032-19105-2"],"isbn":["978-3-032-19104-5"],"eissn":["1865-0937"],"issn":["1865-0929"]},"series_title":"Communications in Computer and Information Science","publication_status":"published","publication":"Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV","status":"public","conference":{"start_date":"2025-09-15","end_date":"2025-09-19","name":"ECML PKDD 2025","location":"Porto, Portugal"},"citation":{"bibtex":"@inproceedings{Jaster_Tharwat_Sheikh_Kohlhase_Schenck_2026, place={Cham}, series={Communications in Computer and Information Science}, title={Low Query Budget Active Learning for Classification and Regression}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">10.1007/978-3-032-19105-2_1</a>}, booktitle={Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV}, publisher={Springer Nature Switzerland}, author={Jaster, Bjarne and Tharwat, Alaa and Sheikh, Eiram Mahera and Kohlhase, Martin and Schenck, Wolfram}, editor={Koprinska, Irena and Mendes-Moreira, João and Branco, PaulaEditors}, year={2026}, pages={5–21}, collection={Communications in Computer and Information Science} }","apa":"Jaster, B., Tharwat, A., Sheikh, E. M., Kohlhase, M., &#38; Schenck, W. (2026). Low Query Budget Active Learning for Classification and Regression. In I. Koprinska, J. Mendes-Moreira, &#38; P. Branco (Eds.), <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i> (pp. 5–21). Cham: Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">https://doi.org/10.1007/978-3-032-19105-2_1</a>","alphadin":"<span style=\"font-variant:small-caps;\">Jaster, Bjarne</span> ; <span style=\"font-variant:small-caps;\">Tharwat, Alaa</span> ; <span style=\"font-variant:small-caps;\">Sheikh, Eiram Mahera</span> ; <span style=\"font-variant:small-caps;\">Kohlhase, Martin</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: Low Query Budget Active Learning for Classification and Regression. In: <span style=\"font-variant:small-caps;\">Koprinska, I.</span> ; <span style=\"font-variant:small-caps;\">Mendes-Moreira, J.</span> ; <span style=\"font-variant:small-caps;\">Branco, P.</span> (Hrsg.): <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, <i>Communications in Computer and Information Science</i>. Cham : Springer Nature Switzerland, 2026, S. 5–21","ieee":"B. Jaster, A. Tharwat, E. M. Sheikh, M. Kohlhase, and W. Schenck, “Low Query Budget Active Learning for Classification and Regression,” in <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, Porto, Portugal, 2026, pp. 5–21.","ama":"Jaster B, Tharwat A, Sheikh EM, Kohlhase M, Schenck W. Low Query Budget Active Learning for Classification and Regression. In: Koprinska I, Mendes-Moreira J, Branco P, eds. <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2026:5-21. doi:<a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">10.1007/978-3-032-19105-2_1</a>","mla":"Jaster, Bjarne, et al. “Low Query Budget Active Learning for Classification and Regression.” <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, edited by Irena Koprinska et al., Springer Nature Switzerland, 2026, pp. 5–21, doi:<a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">10.1007/978-3-032-19105-2_1</a>.","short":"B. Jaster, A. Tharwat, E.M. Sheikh, M. Kohlhase, W. Schenck, in: I. Koprinska, J. Mendes-Moreira, P. Branco (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV, Springer Nature Switzerland, Cham, 2026, pp. 5–21.","chicago":"Jaster, Bjarne, Alaa Tharwat, Eiram Mahera Sheikh, Martin Kohlhase, and Wolfram Schenck. “Low Query Budget Active Learning for Classification and Regression.” In <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, edited by Irena Koprinska, João Mendes-Moreira, and Paula Branco, 5–21. Communications in Computer and Information Science. Cham: Springer Nature Switzerland, 2026. <a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">https://doi.org/10.1007/978-3-032-19105-2_1</a>."},"author":[{"full_name":"Jaster, Bjarne","orcid":"0000-0002-8362-5369","last_name":"Jaster","id":"252434","first_name":"Bjarne"},{"first_name":"Alaa","id":"238549","full_name":"Tharwat, Alaa","last_name":"Tharwat"},{"last_name":"Sheikh","full_name":"Sheikh, Eiram Mahera","first_name":"Eiram Mahera"},{"id":"226669","first_name":"Martin","full_name":"Kohlhase, Martin","orcid":"0009-0002-9374-0720","last_name":"Kohlhase"},{"last_name":"Schenck","full_name":"Schenck, Wolfram","orcid":"0000-0003-3300-2048","id":"224375","first_name":"Wolfram"}],"place":"Cham","date_updated":"2026-06-01T15:18:27Z","page":"5-21","publisher":"Springer Nature Switzerland","doi":"10.1007/978-3-032-19105-2_1","language":[{"iso":"eng"}],"editor":[{"first_name":"Irena","full_name":"Koprinska, Irena","last_name":"Koprinska"},{"first_name":"João","full_name":"Mendes-Moreira, João","last_name":"Mendes-Moreira"},{"full_name":"Branco, Paula","last_name":"Branco","first_name":"Paula"}],"_id":"6898","year":"2026","abstract":[{"lang":"eng","text":"The labeling process for supervised learning is costly and time-consuming, and is often impractical to scale due to real-world constraints. Active learning (AL) addresses this challenge by strategically selecting representative and informative data points to reduce labeling efforts. This paper focuses on an AL scenario in which only a very limited number of labels can be acquired. We propose an algorithm operating in two phases: (1) an exploration phase that prioritizes representative and diverse data points using density-driven criteria, and (2) an exploitation phase that combines predictive uncertainty with density weighting to select informative samples from densely populated regions. This enhances both representativeness and informativeness. Our results demonstrate significant improvements in model quality compared to other algorithms typically employed for this scenario, across various scenarios involving imbalanced data in classification tasks and skewness in regression tasks. Through this work, we aim to provide a new algorithm for this scenario and investigate general principles for AL. While most AL studies focus on either classification or regression, our work applies the algorithms to both. Therefore, we can analyze the differences between classification and regression problems and their effects on AL strategies. Furthermore, we explore different categories of AL criteria and their effectiveness in the low-budget regime. These results also provide insight into the cold-start problem, which involves selecting an initial labeled set and is faced by many model-based AL methods."}]},{"date_updated":"2026-06-01T15:11:58Z","publisher":"Elsevier BV","doi":"10.1016/j.engappai.2026.114295","intvolume":"       171","status":"public","citation":{"ama":"Tharwat A, Jaster B, Schenck W, Kohlhase M. Active learning evaluation metrics for classification and regression frameworks. <i>Engineering Applications of Artificial Intelligence</i>. 2026;171. doi:<a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">10.1016/j.engappai.2026.114295</a>","mla":"Tharwat, Alaa, et al. “Active Learning Evaluation Metrics for Classification and Regression Frameworks.” <i>Engineering Applications of Artificial Intelligence</i>, vol. 171, 114295, Elsevier BV, 2026, doi:<a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">10.1016/j.engappai.2026.114295</a>.","chicago":"Tharwat, Alaa, Bjarne Jaster, Wolfram Schenck, and Martin Kohlhase. “Active Learning Evaluation Metrics for Classification and Regression Frameworks.” <i>Engineering Applications of Artificial Intelligence</i> 171 (2026). <a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">https://doi.org/10.1016/j.engappai.2026.114295</a>.","short":"A. Tharwat, B. Jaster, W. Schenck, M. Kohlhase, Engineering Applications of Artificial Intelligence 171 (2026).","apa":"Tharwat, A., Jaster, B., Schenck, W., &#38; Kohlhase, M. (2026). Active learning evaluation metrics for classification and regression frameworks. <i>Engineering Applications of Artificial Intelligence</i>, <i>171</i>. <a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">https://doi.org/10.1016/j.engappai.2026.114295</a>","bibtex":"@article{Tharwat_Jaster_Schenck_Kohlhase_2026, title={Active learning evaluation metrics for classification and regression frameworks}, volume={171}, DOI={<a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">10.1016/j.engappai.2026.114295</a>}, number={114295}, journal={Engineering Applications of Artificial Intelligence}, publisher={Elsevier BV}, author={Tharwat, Alaa and Jaster, Bjarne and Schenck, Wolfram and Kohlhase, Martin}, year={2026} }","alphadin":"<span style=\"font-variant:small-caps;\">Tharwat, Alaa</span> ; <span style=\"font-variant:small-caps;\">Jaster, Bjarne</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span> ; <span style=\"font-variant:small-caps;\">Kohlhase, Martin</span>: Active learning evaluation metrics for classification and regression frameworks. 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Migenda, R. Möller, and W. Schenck, “H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality,” <i>PLOS One</i>, vol. 21, no. 1, 2026.","alphadin":"<span style=\"font-variant:small-caps;\">Migenda, Nico</span> ; <span style=\"font-variant:small-caps;\">Möller, Ralf</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. In: <i>PLOS One</i> Bd. 21, Public Library of Science (PLoS) (2026), Nr. 1","apa":"Migenda, N., Möller, R., &#38; Schenck, W. (2026). 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Migenda, R. Möller, W. Schenck, PLOS One 21 (2026).","mla":"Migenda, Nico, et al. “H-NGPCA: Hierarchical Clustering of Data Streams with Adaptive Number of Clusters and Adaptive Dimensionality.” <i>PLOS One</i>, vol. 21, no. 1, e0339171, Public Library of Science (PLoS), 2026, doi:<a href=\"https://doi.org/10.1371/journal.pone.0339171\">10.1371/journal.pone.0339171</a>.","ama":"Migenda N, Möller R, Schenck W. H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. <i>PLOS One</i>. 2026;21(1). doi:<a href=\"https://doi.org/10.1371/journal.pone.0339171\">10.1371/journal.pone.0339171</a>"},"author":[{"last_name":"Migenda","full_name":"Migenda, Nico","orcid":"0000-0002-7223-1735","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-7223-1735/work/203835768","id":"218473","first_name":"Nico"},{"last_name":"Möller","full_name":"Möller, Ralf","first_name":"Ralf"},{"first_name":"Wolfram","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/203835770","id":"224375","last_name":"Schenck","orcid":"0000-0003-3300-2048","full_name":"Schenck, Wolfram"}]},{"issue":"10","publication_status":"published","urn":"urn:nbn:de:hbz:bi10-62444","keyword":["prescriptive analytics","prescriptive platforms","advanced data analytics","retrieval-augmented generation","graph-based retrieval-augmented generation","large language models","generative AI","genAI","recommender system"],"project":[{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"},{"_id":"f432a2ee-bceb-11ed-a251-a83585c5074d","name":"Institute for Data Science Solutions"}],"has_accepted_license":"1","oa":"1","main_file_link":[{"url":"https://www.mdpi.com/2504-2289/9/10/261/pdf","open_access":"1"}],"article_type":"original","_id":"6244","language":[{"iso":"eng"}],"publisher":"MDPI AG","date_updated":"2026-06-01T15:15:50Z","citation":{"ieee":"M. Niederhaus, N. Migenda, J. Weller, M. Kohlhase, and W. Schenck, “Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems,” <i>Big Data and Cognitive Computing</i>, vol. 9, no. 10, 2025.","apa":"Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., &#38; Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. <i>Big Data and Cognitive Computing</i>, <i>9</i>(10). <a href=\"https://doi.org/10.3390/bdcc9100261\">https://doi.org/10.3390/bdcc9100261</a>","bibtex":"@article{Niederhaus_Migenda_Weller_Kohlhase_Schenck_2025, title={Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems}, volume={9}, DOI={<a href=\"https://doi.org/10.3390/bdcc9100261\">10.3390/bdcc9100261</a>}, number={10261}, journal={Big Data and Cognitive Computing}, publisher={MDPI AG}, author={Niederhaus, Marvin and Migenda, Nico and Weller, Julian and Kohlhase, Martin and Schenck, Wolfram}, year={2025} }","alphadin":"<span style=\"font-variant:small-caps;\">Niederhaus, Marvin</span> ; <span style=\"font-variant:small-caps;\">Migenda, Nico</span> ; <span style=\"font-variant:small-caps;\">Weller, Julian</span> ; <span style=\"font-variant:small-caps;\">Kohlhase, Martin</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. 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Institute for Data Science Solutions.","ama":"Leuering J, Jalil F, Ahmed QA, Schenck W, Jungeblut T. Cognitive Edge Computing for Multi-Sensor Applications with Sparse Data and High Latency Requirements. In: Bielefeld: Institute for Data Science Solutions.","short":"J. Leuering, F. Jalil, Q.A. Ahmed, W. Schenck, T. 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