{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:18:40Z","timestamp":1758079120689,"version":"3.44.0"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>Real-world datasets often suffer from multiple quality issues, hindering downstream model performance and increasing cleaning costs. To address this, we propose DemandClean, a reinforcement learning-based adaptive data cleaning framework that dynamically balances cleaning effectiveness and operational costs. DemandClean explicitly considers data authenticity (alignment with real-world facts), diversity (richness of feature values), and downstream models' noise tolerance. We categorize data errors as missing (reducing authenticity and diversity), semantic (affecting only authenticity), and syntactic (affecting authenticity but potentially increasing diversity). Based on these errors, DemandClean intelligently selects among Repair, Delete, or No actions, guided by error rates and model robustness. For interpretability, the framework visually distinguishes authenticity, diversity, and tolerance. Extensive experiments confirm that DemandClean achieves near-optimal accuracy at substantially reduced preprocessing costs. Specifically, it reduces repair actions by 80.0% and deletions by 80.7% compared to \"Repair All\" strategies, while maintaining or even exceeding their predictive performance, thus offering an interpretable, cost-effective, and scalable solution for practical applications.<\/jats:p>","DOI":"10.14778\/3750601.3750666","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:37:51Z","timestamp":1758029871000},"page":"5339-5342","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DemandClean: A Multi-Objective Learning Framework for Balancing Model Tolerance to Data Authenticity and Diversity"],"prefix":"10.14778","volume":"18","author":[{"given":"Zekai","family":"Qian","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoou","family":"Ding","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589302"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3704965.3704987"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685879"},{"key":"e_1_2_1_4_1","volume-title":"Boost-Clean: Automated Error Detection and Repair for Machine Learning. CoRR abs\/1711.01299","author":"Krishnan Sanjay","year":"2017","unstructured":"Sanjay Krishnan, Michael J. Franklin, Ken Goldberg, and Eugene Wu. 2017. Boost-Clean: Automated Error Detection and Repair for Machine Learning. CoRR abs\/1711.01299 (2017). arXiv:1711.01299"},{"key":"e_1_2_1_5_1","volume-title":"CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks. In 37th IEEE International Conference on Data Engineering, ICDE","author":"Li Peng","year":"2021","unstructured":"Peng Li, Xi Rao, Jennifer Blase, Yue Zhang, Xu Chu, and Ce Zhang. 2021. CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks. In 37th IEEE International Conference on Data Engineering, ICDE 2021. IEEE, 13\u201324."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3675034.3675051"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137631"},{"key":"e_1_2_1_8_1","volume-title":"Harnessing Diversity for Important Data Selection in Pretraining Large Language Models. In The Thirteenth International Conference on Learning Representations.","author":"Zhang Chi","year":"2025","unstructured":"Chi Zhang, Huaping Zhong, Kuan Zhang, Chengliang Chai, Rui Wang, Xinlin Zhuang, Tianyi Bai, Qiu Jiantao, Lei Cao, Ju Fan, Ye Yuan, Guoren Wang, and Conghui He. 2025. Harnessing Diversity for Important Data Selection in Pretraining Large Language Models. In The Thirteenth International Conference on Learning Representations."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3750601.3750666","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:16Z","timestamp":1758029896000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3750601.3750666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.14778\/3750601.3750666"],"URL":"https:\/\/doi.org\/10.14778\/3750601.3750666","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"2025-09-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}