{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:53:33Z","timestamp":1774540413709,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030739720","type":"print"},{"value":"9783030739737","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-73973-7_4","type":"book-chapter","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T14:03:24Z","timestamp":1617977004000},"page":"34-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Alternative Exploitation of Isolation Forests for Outlier Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9468-5298","authenticated-orcid":false,"given":"Antonella","family":"Mensi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessio","family":"Franzoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5153-9087","authenticated-orcid":false,"given":"David M. J.","family":"Tax","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-2222-3333-4444","authenticated-orcid":false,"given":"Manuele","family":"Bicego","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,10]]},"reference":[{"issue":"9","key":"4_CR1","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1158\/1541-7786.MCR-07-0055","volume":"5","author":"MC Abba","year":"2007","unstructured":"Abba, M.C., et al.: Breast cancer molecular signatures as determined by sage: correlation with lymph node status. Mol. Cancer Res. 5(9), 881\u2013890 (2007)","journal-title":"Mol. Cancer Res."},{"issue":"1","key":"4_CR2","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1145\/2830544.2830549","volume":"17","author":"CC Aggarwal","year":"2015","unstructured":"Aggarwal, C.C., Sathe, S.: Theoretical foundations and algorithms for outlier ensembles. SIGKDD Explor. Newsl. 17(1), 24\u201347 (2015)","journal-title":"SIGKDD Explor. Newsl."},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Bicego, M., Escolano, F.: On learning random forests for random forest-clustering. In: Proceedings of the 25th International Conference on Pattern Recognition, Forthcoming (2021)","DOI":"10.1109\/ICPR48806.2021.9412014"},{"issue":"1","key":"4_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of SIGMOD International Conference on Managing Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"issue":"3","key":"4_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1\u201358 (2009)","journal-title":"ACM Comput. Surv."},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"3490","DOI":"10.1016\/j.patcog.2013.05.022","volume":"46","author":"C D\u00e9sir","year":"2013","unstructured":"D\u00e9sir, C., Bernard, S., Petitjean, C., Heutte, L.: One class random forests. Pattern Recogn. 46, 3490\u20133506 (2013)","journal-title":"Pattern Recogn."},{"issue":"20","key":"4_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3182\/20130902-3-CN-3020.00044","volume":"46","author":"Z Ding","year":"2013","unstructured":"Ding, Z., Fei, M.: An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window. IFAC Proc. 46(20), 12\u201317 (2013)","journal-title":"IFAC Proc."},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Emmott, A.F., Das, S., Dietterich, T., Fern, A., Wong, W.K.: Systematic construction of anomaly detection benchmarks from real data. In: Proceedings of SIGKDD Workshop Outlier Detection and Description, pp. 16\u201321 (2013)","DOI":"10.1145\/2500853.2500858"},{"issue":"1","key":"4_CR10","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.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006)","journal-title":"Mach. Learn."},{"key":"4_CR11","unstructured":"Goix, N., Drougard, N., Brault, R., Chiapino, M.: One class splitting criteria for random forests. In: Proceedings of 9th Asian Conference Machine Learning, vol. 77, pp. 343\u2013358 (2017)"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neuroimage.2012.09.065","volume":"65","author":"KR Gray","year":"2013","unstructured":"Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D.: Random forest-based similarity measures for multi-modal classification of Alzheimer\u2019s disease. NeuroImage 65, 167\u2013175 (2013)","journal-title":"NeuroImage"},{"key":"4_CR13","unstructured":"Guha, S., Mishra, N., Roy, G., Schrijvers, O.: Robust random cut forest based anomaly detection on streams. In: Proceedings of the 33rd International Conference on Machine Learning, vol. 48, pp. 2712\u20132721 (2016)"},{"key":"4_CR14","unstructured":"Hariri, S., Kind, M.C., Brunner, R.J.: Extended isolation forest (2018). arXiv:1811.02141"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Keller, F., Muller, E., Bohm, K.: HICS: high contrast subspaces for density-based outlier ranking. In: IEEE International Conference on Data Engineering, pp. 1037\u20131048. IEEE (2012)","DOI":"10.1109\/ICDE.2012.88"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: IEEE International Conference on Data Mining, pp. 413\u2013422 (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: On detecting clustered anomalies using sciforest. In: ECML PKDD, pp. 274\u2013290 (2010)","DOI":"10.1007\/978-3-642-15883-4_18"},{"issue":"1","key":"4_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2133360.2133363","volume":"6","author":"FT Liu","year":"2012","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. ACM Trans. Knowl. Discov. Data 6(1), 1\u201339 (2012)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Mensi, A., Bicego, M.: A novel anomaly score for isolation forests. In: International Conference on Image Analysis and Processing, pp. 152\u2013163 (2019)","DOI":"10.1007\/978-3-030-30642-7_14"},{"key":"4_CR20","unstructured":"Micenkov\u00e1, B., McWilliams, B., Assent, I.: Learning outlier ensembles: the best of both worlds-supervised and unsupervised. In: Proceedings of SIGKDD Workshop on Outlier Detection and Description, pp. 51\u201354 (2014)"},{"issue":"3","key":"4_CR21","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1513\/AnnalsATS.201403-125OC","volume":"12","author":"S Rennard","year":"2015","unstructured":"Rennard, S., et al.: Identification of five chronic obstructive pulmonary disease subgroups with different prognoses in the eclipse cohort using cluster analysis. Ann. Am. Thorac. Soc. 12(3), 303\u2013312 (2015)","journal-title":"Ann. Am. Thorac. Soc."},{"key":"4_CR22","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/modpathol.3800322","volume":"18","author":"T Shi","year":"2005","unstructured":"Shi, T., Seligson, D., Belldegrun, A., Palotie, A., Horvath, S.: Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma. Modern Pathol. 18, 547\u2013557 (2005)","journal-title":"Modern Pathol."},{"key":"4_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1198\/106186006X94072","volume":"15","author":"T Shi","year":"2006","unstructured":"Shi, T., Horvath, S.: Unsupervised learning with random forest predictors. J. Comput. Graph. Stat. 15, 1\u201321 (2006)","journal-title":"J. Comput. Graph. Stat."},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Susto, G.A., Beghi, A., McLoone, S.: Anomaly detection through on-line isolation forest: an application to plasma etching. In: Annual SEMI Advanced Semiconductor Manufacturing Conference (2017)","DOI":"10.23919\/MIPRO.2017.7966552"},{"key":"4_CR25","unstructured":"Tax, D.: One-class classification; concept-learning in the absence of counter-examples. Ph.D. thesis, Delft University of Technology (2001)"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Ting, K., Zhu, Y., Carman, M., Zhu, Y., Zhou, Z.H.: Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 1205\u20131214 (2016)","DOI":"10.1145\/2939672.2939779"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, X., Loy, C., Gong, S.: Constructing robust affinity graphs for spectral clustering. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1450\u20131457 (2014)","DOI":"10.1109\/CVPR.2014.188"}],"container-title":["Lecture Notes in Computer Science","Structural, Syntactic, and Statistical Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73973-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T22:02:51Z","timestamp":1744149771000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-73973-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030739720","9783030739737"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73973-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"S+SSPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sspr2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dais.unive.it\/sspr2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"81","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}