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International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV","publication_status":"published","series_title":"Communications in Computer and Information Science","oa":"1","project":[{"name":"Institute for Data Science Solutions","_id":"f432a2ee-bceb-11ed-a251-a83585c5074d"},{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"type":"conference","publication_identifier":{"isbn":["978-3-032-19104-5"],"eissn":["1865-0937"],"eisbn":["978-3-032-19105-2"],"issn":["1865-0929"]},"main_file_link":[{"open_access":"1","url":"https://rdcu.be/fh03T"}],"alternative_id":["6899"],"user_id":"226669","date_created":"2026-05-10T07:55:29Z","title":"Low Query Budget Active Learning for Classification and Regression","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."}],"_id":"6898","editor":[{"first_name":"Irena","full_name":"Koprinska, Irena","last_name":"Koprinska"},{"last_name":"Mendes-Moreira","full_name":"Mendes-Moreira, João","first_name":"João"},{"first_name":"Paula","last_name":"Branco","full_name":"Branco, Paula"}],"year":"2026","language":[{"iso":"eng"}],"doi":"10.1007/978-3-032-19105-2_1","date_updated":"2026-06-01T15:18:27Z","page":"5-21","publisher":"Springer Nature Switzerland","citation":{"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.","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>","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} }","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","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>.","ama":"Jaster B, Tharwat A, Sheikh EM, Kohlhase M, Schenck W. Low Query Budget Active Learning for Classification and Regression. 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Kohlhase, “Active learning evaluation metrics for classification and regression frameworks,” <i>Engineering Applications of Artificial Intelligence</i>, vol. 171, 2026."},"author":[{"id":"238549","first_name":"Alaa","full_name":"Tharwat, Alaa","last_name":"Tharwat"},{"first_name":"Bjarne","id":"252434","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-8362-5369/work/207108820","orcid":"0000-0002-8362-5369","full_name":"Jaster, Bjarne","last_name":"Jaster"},{"last_name":"Schenck","orcid":"0000-0003-3300-2048","full_name":"Schenck, Wolfram","first_name":"Wolfram","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/207108821","id":"224375"},{"orcid":"0009-0002-9374-0720","full_name":"Kohlhase, Martin","last_name":"Kohlhase","first_name":"Martin","orcid_put_code_url":"https://api.orcid.org/v2.0/0009-0002-9374-0720/work/207108822","id":"226669"}]},{"type":"conference","project":[{"_id":"f432a2ee-bceb-11ed-a251-a83585c5074d","name":"Institute for Data Science Solutions"}],"user_id":"222105","publication_status":"published","publication":"46. Tagung des Arbeitskreises „Steuern und Wirtschaftsprüfung“ der Professorinnen und Professoren an Hochschulen für angewandte Wissenschaften, Bad Mergentheim, 11.-13.05.","keyword":["Wirtschaftsprüfung","Rechnungswesen","Künstliche Intelligenz","Fachkräftemangel","Hochschullehre","Data Literacy","Automatisierung"],"title":"Digitalisierung und KI in der Abschlussprüfung: Änderungen in Rechnungslegung und Wirtschaftsprüfung und ihre Auswirkungen auf die Hochschulausbildung","date_created":"2026-05-13T13:40:41Z","year":"2026","_id":"6904","language":[{"iso":"ger"}],"abstract":[{"text":"Der Berufsstand der Wirtschaftsprüfer und das Rechnungswesen von Unternehmen kämpfen aktuell mit einem sinkenden Image sowie einem massiven Fachkräftemangel. Als zentrale Antwort darauf gilt die fortschreitende Digitalisierung und der Einsatz von KI, die bereits heute zu einer weitgehenden Automatisierung operativer Kernprozesse wie dem „Purchase-to-Pay“-Porzess führen. \r\nWährend das Institut für Arbeitsmarkt- und Berufsforschung (IAB) für Buchhaltungstätigkeiten ein Automatisierungspotenzial von 100 % sieht, liegt dieses in der Wirtschaftsprüfung bei etwa 57 %. In der Prüfungspraxis verschiebt sich der Fokus von Routineaufgaben hin zu risikoorientierten Datenanalysen, IT-Systemprüfungen und der Nachhaltigkeitsberichterstattung, wobei das finale Urteil weiterhin beim Menschen verbleibt („Human-in-the-Loop“). \r\nFür die Hochschulausbildung bedeutet diese Transformation, dass neben den klassischen Basiskompetenzen vermehrt Spezialwissen in der Datenanalyse sowie übergreifende Meta-Kompetenzen wie „Data Literacy“ und grundlegendes KI-Verständnis vermittelt werden müssen. Hochschulen sind daher gefordert, diesen Wandel durch neue Lehrformate und Kooperationen mit der Praxis aktiv mitzugestalten, um den steigenden qualitativen Anforderungen an künftige Mitarbeitende gerecht zu werden.","lang":"eng"}],"citation":{"chicago":"Kampe, Tim. “Digitalisierung und KI in der Abschlussprüfung: Änderungen in Rechnungslegung und Wirtschaftsprüfung und ihre Auswirkungen auf die Hochschulausbildung.” In <i>46. Tagung des Arbeitskreises „Steuern und Wirtschaftsprüfung“ der Professorinnen und Professoren an Hochschulen für angewandte Wissenschaften, Bad Mergentheim, 11.-13.05.</i>, 2026.","short":"T. Kampe, in: 46. Tagung des Arbeitskreises „Steuern und Wirtschaftsprüfung“ der Professorinnen und Professoren an Hochschulen für angewandte Wissenschaften, Bad Mergentheim, 11.-13.05., 2026.","mla":"Kampe, Tim. “Digitalisierung und KI in der Abschlussprüfung: Änderungen in Rechnungslegung und Wirtschaftsprüfung und ihre Auswirkungen auf die Hochschulausbildung.” <i>46. 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Tagung des Arbeitskreises „Steuern und Wirtschaftsprüfung“ der Professorinnen und Professoren an Hochschulen für angewandte Wissenschaften, Bad Mergentheim, 11.-13.05.</i>, Bad Mergentheim, 2026.","alphadin":"<span style=\"font-variant:small-caps;\">Kampe, Tim</span>: Digitalisierung und KI in der Abschlussprüfung: Änderungen in Rechnungslegung und Wirtschaftsprüfung und ihre Auswirkungen auf die Hochschulausbildung. In: <i>46. Tagung des Arbeitskreises „Steuern und Wirtschaftsprüfung“ der Professorinnen und Professoren an Hochschulen für angewandte Wissenschaften, Bad Mergentheim, 11.-13.05.</i>, 2026","bibtex":"@inproceedings{Kampe_2026, title={Digitalisierung und KI in der Abschlussprüfung: Änderungen in Rechnungslegung und Wirtschaftsprüfung und ihre Auswirkungen auf die Hochschulausbildung}, booktitle={46. 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Siderska, Engineering Management in Production and Services 17 (2025) 51–67.","chicago":"Süße, Thomas, Maria Kobert, and Julia Siderska. “Key Elements of Digital Competence in Professional Sales &#38; Service Work: Development and Evaluation of a Self-Assessment Scale for Frontline Employees.” <i>Engineering Management in Production and Services</i> 17, no. 3 (2025): 51–67. <a href=\"https://doi.org/10.2478/emj-2025-0019\">https://doi.org/10.2478/emj-2025-0019</a>.","mla":"Süße, Thomas, et al. “Key Elements of Digital Competence in Professional Sales &#38; Service Work: Development and Evaluation of a Self-Assessment Scale for Frontline Employees.” <i>Engineering Management in Production and Services</i>, vol. 17, no. 3, Walter de Gruyter GmbH, 2025, pp. 51–67, doi:<a href=\"https://doi.org/10.2478/emj-2025-0019\">10.2478/emj-2025-0019</a>.","ama":"Süße T, Kobert M, Siderska J. 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Korte <i>et al.</i>, “Educational Pathways for Industry 5.0: Development and Evaluation of a Collaborative Learning Cube Defining Learning Objectives, Curriculum Structure, and Certification Processes Across Different Professional Levels,” in <i>Hybrid Human-AI Collaborative Networks. 26th IFIP WG 5.5 SOCOLNET Working Conference on Virtual Enterprises, PRO-VE 2025, Porto, Portugal, October 27-29, 2025, Proceedings, Part II</i>, L. M. Camarinha-Matos, A. Ortiz, X. Boucher, and A. L. Soares, Eds. 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