{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:07:02Z","timestamp":1755839222258},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>\n            The increasing complexity of data analysis tasks makes it dependent on human expertise and challenging for non-experts. One of the major challenges faced in data analysis is the selection of the proper algorithm for given tasks and data sets. Motivated by this, we develop Assassin, aiming at helping users without enough expertise to\n            <jats:italic>automatically select optimal algorithms<\/jats:italic>\n            for classification tasks. By embedding meta-learning techniques and reinforced policy, our system can automatically extract experiences from previous tasks and train a meta-classifier to implement algorithm recommendations. Then we apply genetic search to explore hyperparameter configuration for the selected algorithm. We demonstrate Assassin with classification tasks from OpenML. The system chooses an appropriate algorithm and optimal hyperparameter configuration for them to achieve a high-level performance target. The Assassin has a user-friendly interface that allows users to customize the parameters during the search process.\n          <\/jats:p>","DOI":"10.14778\/3476311.3476336","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T22:48:56Z","timestamp":1635461336000},"page":"2751-2754","source":"Crossref","is-referenced-by-count":6,"title":["Assassin"],"prefix":"10.14778","volume":"14","author":[{"given":"Tianyu","family":"Mu","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghe","family":"Zheng","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Liang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyun","family":"Tang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188395"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357896"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2015.43"},{"volume-title":"Automated Machine Learning","author":"Feurer Matthias","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.07.005"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih Koray Kavukcuoglu etal 2015. Human-level control through deep reinforcement learning. nature 518 7540 (2015) 529--533.  Volodymyr Mnih Koray Kavukcuoglu et al. 2015. Human-level control through deep reinforcement learning. nature 518 7540 (2015) 529--533.","DOI":"10.1038\/nature14236"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1068009.1068244"},{"volume-title":"Selecting Models from Data","author":"Schaffer Cullen","key":"e_1_2_1_8_1"},{"volume-title":"Automated Machine Learning","key":"e_1_2_1_9_1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00200"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415542"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3476311.3476336","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:29:48Z","timestamp":1672226988000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3476311.3476336"}},"subtitle":["an automatic classification system based on algorithm selection"],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":11,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10.14778\/3476311.3476336"],"URL":"https:\/\/doi.org\/10.14778\/3476311.3476336","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}