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Association for Computing Machinery, New York, NY, USA, 2661\u20132668."},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1109\/ICSME52107.2021.00071","volume-title":"2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)","author":"Lewis Grace\u00a0A.","year":"2021","unstructured":"Grace\u00a0A. Lewis, Ipek Ozkaya, and Xiwei Xu. 2021. Software Architecture Challenges for ML Systems. In 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 634\u2013638. 10.1109\/ICSME52107.2021.00071"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Jundong Li Kewei Cheng Suhang Wang Fred Morstatter Robert\u00a0P Trevino Jiliang Tang and Huan Liu. 2017. Feature selection: A data perspective. 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IEEE, 57\u201366."},{"key":"e_1_3_3_2_60_2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/ICT4S55073.2022.00015","volume-title":"2022 international conference on ICT for sustainability (ICT4S)","author":"Verdecchia Roberto","year":"2022","unstructured":"Roberto Verdecchia, Lu\u00eds Cruz, June Sallou, Michelle Lin, James Wickenden, and Estelle Hotellier. 2022. Data-centric green ai an exploratory empirical study. In 2022 international conference on ICT for sustainability (ICT4S). IEEE, 35\u201345."},{"key":"e_1_3_3_2_61_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/SEAMS51251.2021.00036","volume-title":"2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","author":"Weyns Danny","year":"2021","unstructured":"Danny Weyns, Bradley Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson, and Kenji Tei. 2021. Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning. In 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE, 217\u2013223."},{"key":"e_1_3_3_2_62_2","series-title":"(WWW \u201920)","first-page":"928","volume-title":"Proceedings of The Web Conference 2020","author":"Zhu Yongchun","year":"2020","unstructured":"Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, and Qing He. 2020. Modeling Users\u2019 Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection. In Proceedings of The Web Conference 2020(WWW \u201920). Association for Computing Machinery, New York, NY, USA, 928\u2013938."},{"key":"e_1_3_3_2_63_2","unstructured":"I. \u017dliobait\u0117. 2010. 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