{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:15:26Z","timestamp":1760188526967,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T00:00:00Z","timestamp":1557187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Transfer learning aims for high accuracy by applying knowledge of source domains for which data collection is easy in order to target domains where data collection is difficult, and has attracted attention in recent years because of its significant potential to enable the application of machine learning to a wide range of real-world problems. However, since the technique is user-dependent, with data prepared as a source domain which in turn becomes a knowledge source for transfer learning, it often involves the adoption of inappropriate data. In such cases, the accuracy may be reduced due to \u201cnegative transfer.\u201d Thus, in this paper, we propose a novel transfer learning method that utilizes the flipping output technique to provide multiple labels in the source domain. The accuracy of the proposed method is statistically demonstrated to be significantly better than that of the conventional transfer learning method, and its effect size is as high as 0.9, showing high performance.<\/jats:p>","DOI":"10.3390\/a12050095","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Source Domain Extension Method for Inductive Transfer Learning Based on Flipping Output"],"prefix":"10.3390","volume":"12","author":[{"given":"Yasutake","family":"Koishi","sequence":"first","affiliation":[{"name":"Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Saga 841-0052, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuichi","family":"Ishida","sequence":"additional","affiliation":[{"name":"Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Saga 841-0052, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1947-1924","authenticated-orcid":false,"given":"Tatsuo","family":"Tabaru","sequence":"additional","affiliation":[{"name":"Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Saga 841-0052, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroyuki","family":"Miyamoto","sequence":"additional","affiliation":[{"name":"Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), Fukuoka 808-0196, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). Imagenet classification with deep conventional neural networks. Advances in Neural Information Processing Systems 25 (NIPS25), Curran Associates Inc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"Rich","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kamishima, T., Hamasaki, M., and Akaho, S. (2009, January 6\u20139). TrBagg: A simple transfer learning method and its application to personalization in collaborative tagging. 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(2010, January 15). Frustratingly easy semi-supervised domain adoption. Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing, Uppsala, Sweden."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/5\/95\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:49:44Z","timestamp":1760186984000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/5\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,7]]},"references-count":18,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["a12050095"],"URL":"https:\/\/doi.org\/10.3390\/a12050095","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2019,5,7]]}}}