{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:38:02Z","timestamp":1774121882243,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002509","name":"Keimyung University","doi-asserted-by":"publisher","award":["2021"],"award-info":[{"award-number":["2021"]}],"id":[{"id":"10.13039\/501100002509","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10489-021-02451-x","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T21:22:27Z","timestamp":1620076947000},"page":"471-481","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Lightweight surrogate random forest support for model simplification and feature relevance"],"prefix":"10.1007","volume":"52","author":[{"given":"Sangwon","family":"Kim","sequence":"first","affiliation":[]},{"given":"Mira","family":"Jeong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7284-0768","authenticated-orcid":false,"given":"Byoung Chul","family":"Ko","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"2451_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi A, Berrada M (2018) Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access 6:52138\u201352160","journal-title":"IEEE Access"},{"key":"2451_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta AB, et al. (2020) Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. ELSEVIER Inf Fusion 58:82\u2013115","journal-title":"ELSEVIER Inf Fusion"},{"key":"2451_CR3","doi-asserted-by":"crossref","unstructured":"Tan S et al (2018) Distill-and-compare: auditing black-box models using transparent model distillation. In: 2018 AAAI\/ACM conference on AI, ethics and society. pp 303\u2013310","DOI":"10.1145\/3278721.3278725"},{"key":"2451_CR4","unstructured":"Bastani O, Kim C, Bastani H. (2017) Interpretability via model extraction. arXiv:1706.09773"},{"key":"2451_CR5","unstructured":"Zagoruyko S, Komodakis N (2017) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: ICLR, pp 1\u201311"},{"key":"2451_CR6","unstructured":"Xu K et al (2018) Interpreting deep classifier by visual distillation of dark knowledge. arXiv:1803.04042"},{"key":"2451_CR7","doi-asserted-by":"crossref","unstructured":"Kim S, Jeong M, Ko BC (2020) Interpretation and simplification of deep forest. TechRxiv, techrxiv. 11661246.v1","DOI":"10.36227\/techrxiv.11661246.v1"},{"key":"2451_CR8","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1007\/s11590-019-01428-7","volume":"14","author":"S Kim","year":"2020","unstructured":"Kim S, Boukouvala F (2020) Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques. Springer Optim Lett 14:989\u20131010","journal-title":"Springer Optim Lett"},{"key":"2451_CR9","first-page":"1","volume":"20","author":"S Kim","year":"2020","unstructured":"Kim S, Jeong M, Ko BC (2020) Energy efficient pupil tracking based on rule distillation of cascade regression forest. MDPI Sensors 20:1\u201317","journal-title":"MDPI Sensors"},{"key":"2451_CR10","unstructured":"Kim S, Jeong M, Ko BC (2020) Is the surrogate model interpretable?. In: NeurIPS workshops. pp 1\u20135"},{"key":"2451_CR11","doi-asserted-by":"publisher","first-page":"12415","DOI":"10.1109\/ACCESS.2019.2892425","volume":"7","author":"SJ Kim","year":"2019","unstructured":"Kim SJ, Kwak SY, Ko BC (2019) Fast pedestrian detection in surveillance video based on soft target training of shallow random forest. IEEE ACCESS 7:12415\u201312426","journal-title":"IEEE ACCESS"},{"key":"2451_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forest. Springer Mach Learn 45:5\u201332","journal-title":"Springer Mach Learn"},{"key":"2451_CR13","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"J Friedman","year":"2001","unstructured":"Friedman J (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189\u20131232","journal-title":"Ann Stat"},{"key":"2451_CR14","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: 22nd ACM SIGKDD International conference on knowledge discovery and data mining. pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"2451_CR15","unstructured":"Dorogush AV, Ershov V, Gulin A (2018) CatBoost: gradient boosting with categorical features support. arXiv:1810.11363"},{"key":"2451_CR16","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, et al. (2020) From local explanations to global understanding with explainable AI for trees. Nature Mach Intell 2:56\u201367","journal-title":"Nature Mach Intell"},{"key":"2451_CR17","doi-asserted-by":"crossref","unstructured":"Shapley LS (1953) A value for n-person games. In: Contributions to the theory of games, vol 2, pp 307\u2013317","DOI":"10.1515\/9781400881970-018"},{"key":"2451_CR18","unstructured":"Dua D, Graff C (2019) UCI Machine learning repository"},{"key":"2451_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-017-0154-4","volume":"10","author":"RS Olson","year":"2017","unstructured":"Olson RS, et al. (2017) PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData mining 10:1\u201313","journal-title":"BioData mining"},{"key":"2451_CR20","unstructured":"Erickson N et al (2020) AutoGluon-tabular: robust and accurate automl for structured data. arXiv:2003.06505"},{"key":"2451_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3\u201342","journal-title":"Mach Learn"},{"key":"2451_CR22","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"3","author":"DL Wilson","year":"1972","unstructured":"Wilson DL (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern 3:408\u2013421","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"2451_CR23","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik VN (1995) Support-vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"key":"2451_CR24","doi-asserted-by":"crossref","unstructured":"Zhou ZH, Feng J (2017) Deep forest: towards an alternative to deep neural networks. arXiv:1702.08835","DOI":"10.24963\/ijcai.2017\/497"},{"key":"2451_CR25","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1995","unstructured":"Freund Y, Schapire R (1995) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"2451_CR26","unstructured":"Ke G et al (2017) Lightgbm: A highly efficient gradient boosting decision tree. In: NeurIPS, pp 3146\u20133154"},{"key":"2451_CR27","unstructured":"Duan T et al (2020) Ngboost: Natural gradient boosting for probabilistic prediction. In: ICML, pp 2690\u20132700"},{"key":"2451_CR28","doi-asserted-by":"crossref","unstructured":"Kokel H et al (2020) A unified framework for knowledge intensive gradient boosting: leveraging human experts for noisy sparse domains. In: AAAI. pp 4460\u20134468","DOI":"10.1609\/aaai.v34i04.5873"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02451-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02451-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02451-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T06:34:07Z","timestamp":1642142047000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02451-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,3]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2451"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02451-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,3]]},"assertion":[{"value":"20 April 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}