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However, training a Deviance Prediction Model (DPM) by solely using supervised learning methods is impractical in scenarios where only few examples are labelled. To address this challenge, we propose an Active-Learning-based approach that leverages multiple DPMs and a temporal ensembling method that can train and merge them in a few training epochs. Our method needs expert supervision only for a few unlabelled traces exhibiting high prediction uncertainty. Tests on real data (of either complete or ongoing process instances) confirm the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1007\/s10844-024-00841-4","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T06:01:36Z","timestamp":1705989696000},"page":"995-1019","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Data- &amp; compute-efficient deviance mining via active learning and fast ensembles"],"prefix":"10.1007","volume":"62","author":[{"given":"Francesco","family":"Folino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianluigi","family":"Folino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Massimo","family":"Guarascio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Pontieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"841_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s40537-021-00419-9","volume":"8","author":"A Adadi","year":"2021","unstructured":"Adadi, A. 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