{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T02:02:22Z","timestamp":1768701742618,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62076120"],"award-info":[{"award-number":["62076120"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"JST CREST","award":["JPMJCR1666"],"award-info":[{"award-number":["JPMJCR1666"]}]},{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI","award":["20K21815"],"award-info":[{"award-number":["20K21815"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s10618-021-00785-1","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T19:03:05Z","timestamp":1629399785000},"page":"2282-2312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Isolation kernel: the X factor in efficient and effective large scale online kernel learning"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7892-6194","authenticated-orcid":false,"given":"Kai Ming","family":"Ting","sequence":"first","affiliation":[]},{"given":"Jonathan R.","family":"Wells","sequence":"additional","affiliation":[]},{"given":"Takashi","family":"Washio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"785_CR1","unstructured":"Breiman L (2000) Some infinity theory for predictor ensembles. Tech Rep 577, Statistics Dept, University of California, Berkeley"},{"issue":"1","key":"785_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"2","key":"785_CR3","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s10994-007-5003-0","volume":"69","author":"G Cavallanti","year":"2007","unstructured":"Cavallanti G, Cesa-Bianchi N, Gentile C (2007) Tracking the best hyperplane with a simple budget perceptron. Mach Learn 69(2):143\u2013167","journal-title":"Mach Learn"},{"issue":"3","key":"785_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Transactions Intell Syst Technol 2(3):1\u201327","journal-title":"ACM Transactions Intell Syst Technol"},{"key":"785_CR5","first-page":"1471","volume":"11","author":"YW Chang","year":"2010","unstructured":"Chang YW, Hsieh CJ, Chang KW, Ringgaard M, Lin CJ (2010) Training and testing low-degree polynomial data mappings via linear svm. J Mach Learn Res 11:1471\u20131490","journal-title":"J Mach Learn Res"},{"key":"785_CR6","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871\u20131874","journal-title":"J Mach Learn Res"},{"key":"785_CR7","unstructured":"Felix XY, Suresh AT, Choromanski KM, Holtmann-Rice DN, Kumar S (2016) Orthogonal random features. In: Advances in Neural Information Processing Systems, pp 1975\u20131983"},{"issue":"1","key":"785_CR8","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(1):3\u201342","journal-title":"Mach Learn"},{"key":"785_CR9","doi-asserted-by":"crossref","unstructured":"Huang P, Avron H, Sainath TN, Sindhwani V, Ramabhadran B (2014) Kernel methods match deep neural networks on timit. In: Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 205\u2013209","DOI":"10.1109\/ICASSP.2014.6853587"},{"key":"785_CR10","unstructured":"Jose C, Goyal P, Aggrwal P, Varma M (2013) Local deep kernel learning for efficient non-linear svm prediction. In: Proceedings of the 30th International Conference on Machine Learning, pp III\u2013486\u2013III\u2013494"},{"issue":"1","key":"785_CR11","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/TPAMI.2017.2785313","volume":"41","author":"M Kafai","year":"2019","unstructured":"Kafai M, Eshghi K (2019) Croification: accurate kernel classification with the efficiency of sparse linear svm. IEEE Transactions Pattern Anal Mach Intell 41(1):34\u201348","journal-title":"IEEE Transactions Pattern Anal Mach Intell"},{"key":"785_CR12","unstructured":"Kivinen J, Smola AJ, Williamson RC (2001) Online learning with kernels. In: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, pp 785\u2013792"},{"key":"785_CR13","doi-asserted-by":"crossref","unstructured":"Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: Proceedings of the IEEE International Conference on Data Mining, pp 413\u2013422","DOI":"10.1109\/ICDM.2008.17"},{"issue":"1","key":"785_CR14","first-page":"1613","volume":"17","author":"J Lu","year":"2016","unstructured":"Lu J, Hoi SCH, Wang J, Zhao P, Liu ZY (2016) Large scale online kernel learning. J Mach Learn Res 17(1):1613\u20131655","journal-title":"J Mach Learn Res"},{"issue":"1","key":"785_CR15","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/TPAMI.2012.62","volume":"35","author":"S Maji","year":"2013","unstructured":"Maji S, Berg AC, Malik J (2013) Efficient classification for additive kernel svms. IEEE Transactions Pattern Anal Mach Intell 35(1):66\u201377","journal-title":"IEEE Transactions Pattern Anal Mach Intell"},{"key":"785_CR16","unstructured":"Musco C, Musco C (2017) Recursive sampling for the nystrom method. In: Advances in Neural Information Processing Systems, pp 3833\u20133845"},{"key":"785_CR17","doi-asserted-by":"crossref","unstructured":"Qin X, Ting KM, Zhu Y, Lee VCS (2019) Nearest-neighbour-induced isolation similarity and its impact on density-based clustering. In: Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence, pp 4755\u20134762","DOI":"10.1609\/aaai.v33i01.33014755"},{"key":"785_CR18","unstructured":"Rahimi A, Recht B (2007) Random features for large-scale kernel machines. In: Advances in Neural Information Processing Systems, pp 1177\u20131184"},{"issue":"Nov","key":"785_CR19","first-page":"2491","volume":"9","author":"A Rakotomamonjy","year":"2008","unstructured":"Rakotomamonjy A, Bach FR, Canu S, Grandvalet Y (2008) SimpleMKL. J Mach Learn Res 9(Nov):2491\u20132521","journal-title":"J Mach Learn Res"},{"key":"785_CR20","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with kernels: support vector machines, regularization, optimization, and beyond","author":"B Scholkopf","year":"2001","unstructured":"Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge"},{"key":"785_CR21","doi-asserted-by":"crossref","unstructured":"Shen Y, Chen T, Giannakis G (2018) Online ensemble multi-kernel learning adaptive to non-stationary and adversarial environments. In: Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, pp 2037\u20132046","DOI":"10.1109\/SPAWC.2018.8445874"},{"key":"785_CR22","doi-asserted-by":"crossref","unstructured":"Ting KM, Zhu Y, Zhou ZH (2018) Isolation kernel and its effect on SVM. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 2329\u20132337","DOI":"10.1145\/3219819.3219990"},{"key":"785_CR23","doi-asserted-by":"crossref","unstructured":"Ting KM, Xu BC, Washio T, Zhou ZH (2020) Isolation distributional kernel: a new tool for kernel based anomaly detection. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 198\u2013206","DOI":"10.1145\/3394486.3403062"},{"issue":"3","key":"785_CR24","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1109\/TPAMI.2011.153","volume":"34","author":"A Vedaldi","year":"2012","unstructured":"Vedaldi A, Zisserman A (2012a) Efficient additive kernels via explicit feature maps. IEEE Transactions Pattern Anal Mach Intell 34(3):480\u2013492","journal-title":"IEEE Transactions Pattern Anal Mach Intell"},{"key":"785_CR25","doi-asserted-by":"crossref","unstructured":"Vedaldi A, Zisserman A (2012b) Sparse kernel approximations for efficient classification and detection. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 2320\u20132327","DOI":"10.1109\/CVPR.2012.6247943"},{"key":"785_CR26","unstructured":"Viet\u00a0Le Q, Sarlos T, Smola AJ (2013) Fastfood: approximate kernel expansions in loglinear time. In: Proceedings of the 30th International Conference on Machine Learning, pp III\u2013244\u2013III\u2013252"},{"key":"785_CR27","unstructured":"Wang Z, Vucetic S (2010) Online passive-aggressive algorithms on a budget. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp 908\u2013915"},{"issue":"1","key":"785_CR28","first-page":"3103","volume":"13","author":"Z Wang","year":"2012","unstructured":"Wang Z, Crammer K, Vucetic S (2012) Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale svm training. J Mach Learn Res 13(1):3103\u20133131","journal-title":"J Mach Learn Res"},{"key":"785_CR29","unstructured":"Williams CKI, Seeger M (2001) Using the nystr\u00f6m method to speed up kernel machines. In: Advances in Neural Information Processing Systems 13. MIT Press, Cambridge, pp 682\u2013688"},{"key":"785_CR30","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.neucom.2016.12.047","volume":"234","author":"J Wu","year":"2017","unstructured":"Wu J, Ding L, Liao S (2017) Predictive nystr\u00f6m method for kernel methods. Neurocomputing 234:116\u2013125","journal-title":"Neurocomputing"},{"key":"785_CR31","doi-asserted-by":"crossref","unstructured":"Xu BC, Ting KM, Zhou ZH (2019) Isolation set-kernel and its application to multi-instance learning. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 941\u2013949","DOI":"10.1145\/3292500.3330830"},{"key":"785_CR32","unstructured":"Xu BC, Ting KM, Jiang Y (2021) Isolation graph kernel. In: Proceedings of The Thirty-Fifth AAAI Conference on Artificial Intelligence, pp 10487\u201310495"},{"key":"785_CR33","doi-asserted-by":"crossref","unstructured":"Yang J, Sindhwani V, Fan Q, Avron H, Mahoney M (2014) Random Laplace feature maps for semigroup kernels on histograms. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 971\u2013978","DOI":"10.1109\/CVPR.2014.129"},{"key":"785_CR34","unstructured":"Yang T, Li YF, Mahdavi M, Jin R, Zhou ZH (2012) Nystr\u00f6m method vs random fourier features: a theoretical and empirical comparison. In: Advances in Neural Information Processing Systems, pp 476\u2013484"},{"key":"785_CR35","unstructured":"Zadeh P, Hosseini R, Sra S (2016) Geometric mean metric learning. In: Proceedings of the International Conference on Machine Learning, pp 2464\u20132471"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-021-00785-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-021-00785-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-021-00785-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T22:31:01Z","timestamp":1725661861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-021-00785-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,19]]},"references-count":35,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["785"],"URL":"https:\/\/doi.org\/10.1007\/s10618-021-00785-1","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,19]]},"assertion":[{"value":"3 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}