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This is achieved via the use of self-adaptive systems and through the execution of adaptation tactics, such as<jats:italic>model retraining<\/jats:italic>, which operate at the level of individual ML components.<\/jats:p><jats:p>To address this problem, we propose a probabilistic modeling framework that reasons about the cost\/benefit tradeoffs associated with adapting ML components. The key idea of the proposed approach is to decouple the problems of estimating (1) the expected performance improvement after adaptation and (2) the impact of ML adaptation on overall system utility.<\/jats:p><jats:p>We apply the proposed framework to engineer a self-adaptive ML-based fraud detection system, which we evaluate using a publicly available, real fraud detection dataset. We initially consider a scenario in which information on the model\u2019s quality is immediately available. Next, we relax this assumption by integrating (and extending) state-of-the-art techniques for estimating the model\u2019s quality in the proposed framework. We show that by predicting the system utility stemming from retraining an ML component, the probabilistic model checker can generate adaptation strategies that are significantly closer to the optimal, as compared against baselines such as periodic or reactive retraining.<\/jats:p>","DOI":"10.1145\/3648682","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:49:26Z","timestamp":1709812166000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Self-adapting Machine Learning-based Systems via a Probabilistic Model Checking Framework"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9616-4821","authenticated-orcid":false,"given":"Maria","family":"Casimiro","sequence":"first","affiliation":[{"name":"S3D, Carnegie Mellon University, Pittsburgh, USA and IST, Universidade de Lisboa, Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2835-2859","authenticated-orcid":false,"given":"Diogo","family":"Soares","sequence":"additional","affiliation":[{"name":"S3D, Carnegie Mellon University, Pittsburgh, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6735-8301","authenticated-orcid":false,"given":"David","family":"Garlan","sequence":"additional","affiliation":[{"name":"S3D, Carnegie Mellon University, Pittsburgh, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0313-6590","authenticated-orcid":false,"given":"Lu\u00eds","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"INESC-ID, IST, Universidade de Lisboa, Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7026-7446","authenticated-orcid":false,"given":"Paolo","family":"Romano","sequence":"additional","affiliation":[{"name":"INESC-ID, IST, Universidade de Lisboa, Lisboa, Portugal"}]}],"member":"320","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"IEEE Computational Intelligence Society. 2019. 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