{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:42:40Z","timestamp":1760028160915,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031220630"},{"type":"electronic","value":"9783031220647"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-22064-7_31","type":"book-chapter","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T19:02:04Z","timestamp":1669230124000},"page":"431-443","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Implementation and\u00a0Analysis of\u00a0Centroid Displacement-Based k-Nearest Neighbors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3691-8652","authenticated-orcid":false,"given":"Alex X.","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1357-0814","authenticated-orcid":false,"given":"Stefanka S.","family":"Chukova","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6203-6664","authenticated-orcid":false,"given":"Binh P.","family":"Nguyen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.patrec.2018.05.001","volume":"134","author":"Z Abu-Aisheh","year":"2020","unstructured":"Abu-Aisheh, Z., Raveaux, R., Ramel, J.Y.: Efficient k-nearest neighbors search in graph space. Pattern Recogn. Lett. 134, 77\u201386 (2020)","journal-title":"Pattern Recogn. Lett."},{"issue":"4","key":"31_CR2","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1089\/big.2018.0175","volume":"7","author":"HA Abu Alfeilat","year":"2019","unstructured":"Abu Alfeilat, H.A., et al.: Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big Data 7(4), 221\u2013248 (2019)","journal-title":"Big Data"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Bentley, J.L.: Survey of techniques for fixed radius near neighbor searching. Technical report, Stanford Linear Accelerator Center, Calif. (USA) (1975)","DOI":"10.2172\/1453938"},{"issue":"2","key":"31_CR4","first-page":"1","volume":"1","author":"SH Cha","year":"2007","unstructured":"Cha, S.H.: Comprehensive survey on distance\/similarity measures between probability density functions. City 1(2), 1 (2007)","journal-title":"City"},{"issue":"4","key":"31_CR5","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/TSMC.1976.5408784","volume":"6","author":"SA Dudani","year":"1976","unstructured":"Dudani, S.A.: The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC 6(4), 325\u2013327 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern. SMC"},{"key":"31_CR6","unstructured":"Elhamifar, E., Vidal, R.: Sparse manifold clustering and embedding. Adv. Neural Inf. Process. Syst. 24 (2011)"},{"key":"31_CR7","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.asoc.2017.02.020","volume":"55","author":"\u00d6F Ertu\u011frul","year":"2017","unstructured":"Ertu\u011frul, \u00d6.F., Ta\u011fluk, M.E.: A novel version of k nearest neighbor: dependent nearest neighbor. Appl. Soft Comput. 55, 480\u2013490 (2017)","journal-title":"Appl. Soft Comput."},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Fix, E., Hodges, J.: Discriminatory analysis, nonparametric discrimination: consistency properties. Technical report 4, USAF School of Aviation Medicine, Randolph Field 1951 (1951)","DOI":"10.1037\/e471672008-001"},{"issue":"3","key":"31_CR9","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/34.990132","volume":"24","author":"TK Ho","year":"2002","unstructured":"Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 289\u2013300 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR10","series-title":"Studies in Big Data","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-33383-0_5","volume-title":"Machine Learning for Evolution Strategies","author":"O Kramer","year":"2016","unstructured":"Kramer, O.: Scikit-learn. In: Machine Learning for Evolution Strategies. SBD, vol. 20, pp. 45\u201353. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-33383-0_5"},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.patrec.2020.10.005","volume":"140","author":"MM Kumbure","year":"2020","unstructured":"Kumbure, M.M., Luukka, P., Collan, M.: A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean. Pattern Recogn. Lett. 140, 172\u2013178 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"31_CR12","unstructured":"Lichman, M., et al.: UCI machine learning repository (2013)"},{"issue":"6","key":"31_CR13","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/THMS.2015.2453203","volume":"45","author":"BP Nguyen","year":"2015","unstructured":"Nguyen, B.P., Tay, W.L., Chui, C.K.: Robust biometric recognition from palm depth images for gloved hands. IEEE Trans. Hum.-Mach. Syst. 45(6), 799\u2013804 (2015)","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Pan, Z., Wang, Y., Pan, Y.: A new locally adaptive k-nearest neighbor algorithm based on discrimination class. Knowl.-Based Syst. 204, 106185 (2020)","DOI":"10.1016\/j.knosys.2020.106185"},{"issue":"2","key":"31_CR15","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","volume":"4","author":"LE Peterson","year":"2009","unstructured":"Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)","journal-title":"Scholarpedia"},{"key":"31_CR16","unstructured":"Raschka, S.: Model evaluation, model selection, and algorithm selection in machine learning. arXiv preprint arXiv:1811.12808 (2018)"},{"key":"31_CR17","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.ins.2020.11.054","volume":"552","author":"Y Ruan","year":"2021","unstructured":"Ruan, Y., Xiao, Y., Hao, Z., Liu, B.: A nearest-neighbor search model for distance metric learning. Inf. Sci. 552, 261\u2013277 (2021)","journal-title":"Inf. Sci."},{"key":"31_CR18","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.patrec.2021.10.005","volume":"155","author":"S Sengupta","year":"2022","unstructured":"Sengupta, S., Das, S.: Selective nearest neighbors clustering. Pattern Recogn. Lett. 155, 178\u2013185 (2022)","journal-title":"Pattern Recogn. Lett."},{"key":"31_CR19","doi-asserted-by":"crossref","unstructured":"Song, Y., Kong, X., Zhang, C.: A large-scale-nearest neighbor classification algorithm based on neighbor relationship preservation. Wireless Commun. Mob. Comput. 2022 (2022)","DOI":"10.1155\/2022\/7409171"},{"issue":"3","key":"31_CR20","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s10844-013-0250-y","volume":"41","author":"BL Sturm","year":"2013","unstructured":"Sturm, B.L.: Classification accuracy is not enough. J. Intell. Inf. Syst. 41(3), 371\u2013406 (2013)","journal-title":"J. Intell. Inf. Syst."},{"issue":"10","key":"31_CR21","doi-asserted-by":"publisher","first-page":"6567","DOI":"10.1073\/pnas.082099299","volume":"99","author":"R Tibshirani","year":"2002","unstructured":"Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. 99(10), 6567\u20136572 (2002)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"31_CR22","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.chemolab.2016.06.013","volume":"157","author":"R Todeschini","year":"2016","unstructured":"Todeschini, R., Ballabio, D., Consonni, V., Grisoni, F.: A new concept of higher-order similarity and the role of distance\/similarity measures in local classification methods. Chemom. Intell. Lab. Syst. 157, 50\u201357 (2016)","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"1","key":"31_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-10358-x","volume":"12","author":"S Uddin","year":"2022","unstructured":"Uddin, S., Haque, I., Lu, H., Moni, M.A., Gide, E.: Comparative performance analysis of k-nearest Neighbour (KNN) algorithm and its different variants for disease prediction. Sci. Rep. 12(1), 1\u201311 (2022)","journal-title":"Sci. Rep."},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., Mueller, A.: Scikit-learn: machine learning without learning the machinery. GetMobile: Mob. Comput. Commun. 19(1), 29\u201333 (2015)","DOI":"10.1145\/2786984.2786995"},{"key":"31_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/3-540-47887-6_10","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Z Xie","year":"2002","unstructured":"Xie, Z., Hsu, W., Liu, Z., Lee, M.L.: SNNB: a selective neighborhood based na\u00efve Bayes for lazy learning. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 104\u2013114. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-47887-6_10"},{"issue":"2","key":"31_CR26","first-page":"4","volume":"2","author":"L Yang","year":"2006","unstructured":"Yang, L., Jin, R.: Distance metric learning: a comprehensive survey. Mich. State Universiy 2(2), 4 (2006)","journal-title":"Mich. State Universiy"},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, R.F., Urbanowicz, R.J.: A scikit-learn compatible learning classifier system. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 1816\u20131823 (2020)","DOI":"10.1145\/3377929.3398097"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22064-7_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T12:31:29Z","timestamp":1728477089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22064-7_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031220630","9783031220647"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22064-7_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2022.uqcloud.net\/index.html","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":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"198","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":"72","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":"36% - 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":"5","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":"3","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)"}}]}}