{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T05:45:23Z","timestamp":1781415923218,"version":"3.54.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031632228","type":"print"},{"value":"9783031632235","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-63223-5_7","type":"book-chapter","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T15:02:36Z","timestamp":1718895756000},"page":"83-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Empirical Analysis of\u00a0Data Reduction Techniques for\u00a0k-NN Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2125-6339","authenticated-orcid":false,"given":"Stylianos","family":"Eleftheriadis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1639-2152","authenticated-orcid":false,"given":"Georgios","family":"Evangelidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1094-2520","authenticated-orcid":false,"given":"Stefanos","family":"Ougiaroglou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"issue":"3","key":"7_CR1","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/S00500-008-0323-Y","volume":"13","author":"J Alcal\u00e1-Fdez","year":"2009","unstructured":"Alcal\u00e1-Fdez, J., et al.: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 13(3), 307\u2013318 (2009). https:\/\/doi.org\/10.1007\/S00500-008-0323-Y","journal-title":"Soft Comput."},{"issue":"11","key":"7_CR2","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.1109\/TKDE.2007.190645","volume":"19","author":"F Angiulli","year":"2007","unstructured":"Angiulli, F.: Fast nearest neighbor condensation for large data sets classification. IEEE Trans. Knowl. Data Eng. 19(11), 1450\u20131464 (2007). https:\/\/doi.org\/10.1109\/TKDE.2007.190645","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"7_CR3","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"TM Cover","year":"1967","unstructured":"Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21\u201327 (1967). https:\/\/doi.org\/10.1109\/TIT.1967.1053964","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"2","key":"7_CR4","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/S0031-3203(96)00072-6","volume":"30","author":"C Decaestecker","year":"1997","unstructured":"Decaestecker, C.: Finding prototypes for nearest neighbour classification by means of gradient descent and deterministic annealing. Pattern Recognit. 30(2), 281\u2013288 (1997). https:\/\/doi.org\/10.1016\/S0031-3203(96)00072-6","journal-title":"Pattern Recognit."},{"key":"7_CR5","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"issue":"4","key":"7_CR6","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1023\/B:HEUR.0000034715.70386.5B","volume":"10","author":"F Fern\u00e1ndez","year":"2004","unstructured":"Fern\u00e1ndez, F., Isasi, P.: Evolutionary design of nearest prototype classifiers. J. Heuristics 10(4), 431\u2013454 (2004). https:\/\/doi.org\/10.1023\/B:HEUR.0000034715.70386.5B","journal-title":"J. Heuristics"},{"issue":"8","key":"7_CR7","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1016\/J.PATCOG.2008.02.006","volume":"41","author":"S Garc\u00eda","year":"2008","unstructured":"Garc\u00eda, S., Cano, J.R., Herrera, F.: A memetic algorithm for evolutionary prototype selection: a scaling up approach. Pattern Recognit. 41(8), 2693\u20132709 (2008). https:\/\/doi.org\/10.1016\/J.PATCOG.2008.02.006","journal-title":"Pattern Recognit."},{"issue":"3","key":"7_CR8","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TPAMI.2011.142","volume":"34","author":"S Garc\u00eda","year":"2012","unstructured":"Garc\u00eda, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417\u2013435 (2012). https:\/\/doi.org\/10.1109\/TPAMI.2011.142","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR9","doi-asserted-by":"publisher","unstructured":"Gates, G.W.: The reduced nearest neighbor rule (corresp.). IEEE Trans. Inf. Theory 18(3), 431\u2013433 (1972). https:\/\/doi.org\/10.1109\/TIT.1972.1054809","DOI":"10.1109\/TIT.1972.1054809"},{"key":"7_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2022.108553","volume":"126","author":"T Giorginis","year":"2022","unstructured":"Giorginis, T., Ougiaroglou, S., Evangelidis, G., Dervos, D.A.: Fast data reduction by space partitioning via convex hull and MBR computation. Pattern Recognit. 126, 108553 (2022). https:\/\/doi.org\/10.1016\/J.PATCOG.2022.108553","journal-title":"Pattern Recognit."},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Hart, P.E.: The condensed nearest neighbor rule (corresp.). IEEE Trans. Inf. Theory 14(3), 515\u2013516 (1968). https:\/\/doi.org\/10.1109\/TIT.1968.1054155","DOI":"10.1109\/TIT.1968.1054155"},{"key":"7_CR12","doi-asserted-by":"publisher","unstructured":"Kasemtaweechok, C., Suwannik, W.: Prototype selection for k-nearest neighbors classification using geometric median. In: Proceedings of the Fifth International Conference on Network, Communication and Computing, ICNCC 2016, Kyoto, Japan, 17\u201321 December 2016, pp. 140\u2013144. ACM (2016). https:\/\/doi.org\/10.1145\/3033288.3033301","DOI":"10.1145\/3033288.3033301"},{"issue":"3","key":"7_CR13","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/S10044-003-0191-0","volume":"6","author":"S Kim","year":"2003","unstructured":"Kim, S., Oommen, B.J.: A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Anal. Appl. 6(3), 232\u2013244 (2003). https:\/\/doi.org\/10.1007\/S10044-003-0191-0","journal-title":"Pattern Anal. Appl."},{"issue":"1","key":"7_CR14","doi-asserted-by":"publisher","first-page":"20108","DOI":"10.1038\/s41598-022-23036-9","volume":"12","author":"J Li","year":"2022","unstructured":"Li, J., Dai, C.: Fast prototype selection algorithm based on adjacent neighbourhood and boundary approximation. Sci. Rep. 12(1), 20108 (2022). https:\/\/doi.org\/10.1038\/s41598-022-23036-9","journal-title":"Sci. Rep."},{"key":"7_CR15","doi-asserted-by":"publisher","unstructured":"Mukahar, N., Rosdi, B.A.: Performance comparison of prototype selection based on edition search for nearest neighbor classification. In: Zamli, K.Z., Mezhuyev, V., Benedicenti, L. (eds.) Proceedings of the 7th International Conference on Software and Computer Applications, ICSCA 2018, Kuantan, Malaysia, 08\u201310 February 2018, pp. 143\u2013146. ACM (2018). https:\/\/doi.org\/10.1145\/3185089.3185145","DOI":"10.1145\/3185089.3185145"},{"issue":"4\u20136","key":"7_CR16","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1016\/J.NEUCOM.2008.03.008","volume":"72","author":"L Nanni","year":"2009","unstructured":"Nanni, L., Lumini, A.: Particle swarm optimization for prototype reduction. Neurocomputing 72(4\u20136), 1092\u20131097 (2009). https:\/\/doi.org\/10.1016\/J.NEUCOM.2008.03.008","journal-title":"Neurocomputing"},{"issue":"2","key":"7_CR17","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/S10044-008-0142-X","volume":"13","author":"JA Olvera-L\u00f3pez","year":"2010","unstructured":"Olvera-L\u00f3pez, J.A., Carrasco-Ochoa, J.A., Trinidad, J.F.M.: A new fast prototype selection method based on clustering. Pattern Anal. Appl. 13(2), 131\u2013141 (2010). https:\/\/doi.org\/10.1007\/S10044-008-0142-X","journal-title":"Pattern Anal. Appl."},{"issue":"3\u20134","key":"7_CR18","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/S10472-015-9472-8","volume":"76","author":"S Ougiaroglou","year":"2016","unstructured":"Ougiaroglou, S., Evangelidis, G.: Efficient editing and data abstraction by finding homogeneous clusters. Ann. Math. Artif. Intell. 76(3\u20134), 327\u2013349 (2016). https:\/\/doi.org\/10.1007\/S10472-015-9472-8","journal-title":"Ann. Math. Artif. Intell."},{"issue":"1","key":"7_CR19","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/S10044-014-0393-7","volume":"19","author":"S Ougiaroglou","year":"2016","unstructured":"Ougiaroglou, S., Evangelidis, G.: RHC: a non-parametric cluster-based data reduction for efficient k-NN classification. Pattern Anal. Appl. 19(1), 93\u2013109 (2016). https:\/\/doi.org\/10.1007\/S10044-014-0393-7","journal-title":"Pattern Anal. Appl."},{"key":"7_CR20","doi-asserted-by":"publisher","unstructured":"Rosero-Montalvo, P., et al.: Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: Empirical study. In: 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), pp.\u00a01\u20135 (2017). https:\/\/doi.org\/10.1109\/ETCM.2017.8247530","DOI":"10.1109\/ETCM.2017.8247530"},{"issue":"7","key":"7_CR21","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1016\/J.PATCOG.2003.12.012","volume":"37","author":"JS S\u00e1nchez","year":"2004","unstructured":"S\u00e1nchez, J.S.: High training set size reduction by space partitioning and prototype abstraction. Pattern Recognit. 37(7), 1561\u20131564 (2004). https:\/\/doi.org\/10.1016\/J.PATCOG.2003.12.012","journal-title":"Pattern Recognit."},{"issue":"6","key":"7_CR22","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/S0167-8655(97)00035-4","volume":"18","author":"JS S\u00e1nchez","year":"1997","unstructured":"S\u00e1nchez, J.S., Pla, F., Ferri, F.J.: Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recognit. Lett. 18(6), 507\u2013513 (1997). https:\/\/doi.org\/10.1016\/S0167-8655(97)00035-4","journal-title":"Pattern Recognit. Lett."},{"key":"7_CR23","doi-asserted-by":"publisher","unstructured":"Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Cohen, W.W., Hirsh, H. (eds.) Machine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, 10\u201313 July 1994, pp. 293\u2013301. Morgan Kaufmann (1994). https:\/\/doi.org\/10.1016\/B978-1-55860-335-6.50043-X","DOI":"10.1016\/B978-1-55860-335-6.50043-X"},{"key":"7_CR24","doi-asserted-by":"publisher","unstructured":"Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC-6(6), 448\u2013452 (1976). https:\/\/doi.org\/10.1109\/TSMC.1976.4309523","DOI":"10.1109\/TSMC.1976.4309523"},{"issue":"1","key":"7_CR25","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TSMCC.2010.2103939","volume":"42","author":"I Triguero","year":"2012","unstructured":"Triguero, I., Derrac, J., Garc\u00eda, S., Herrera, F.: A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Trans. Syst. Man Cybern. Part C 42(1), 86\u2013100 (2012). https:\/\/doi.org\/10.1109\/TSMCC.2010.2103939","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"7_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/978-3-319-58838-4_37","volume-title":"Pattern Recognition and Image Analysis","author":"JJ Valero-Mas","year":"2017","unstructured":"Valero-Mas, J.J., Calvo-Zaragoza, J., Rico-Juan, J.R., I\u00f1esta, J.M.: A study of prototype selection algorithms for\u00a0nearest neighbour in class-imbalanced problems. In: Alexandre, L.A., Salvador S\u00e1nchez, J., Rodrigues, J.M.F. (eds.) IbPRIA 2017. LNCS, vol. 10255, pp. 335\u2013343. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58838-4_37"},{"issue":"10","key":"7_CR27","doi-asserted-by":"publisher","first-page":"2770","DOI":"10.1016\/J.PATCOG.2013.03.004","volume":"46","author":"N Verbiest","year":"2013","unstructured":"Verbiest, N., Cornelis, C., Herrera, F.: FRPS: a fuzzy rough prototype selection method. Pattern Recognit. 46(10), 2770\u20132782 (2013). https:\/\/doi.org\/10.1016\/J.PATCOG.2013.03.004","journal-title":"Pattern Recognit."},{"key":"7_CR28","doi-asserted-by":"publisher","unstructured":"Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. SMC-2(3), 408\u2013421 (1972). https:\/\/doi.org\/10.1109\/TSMC.1972.4309137","DOI":"10.1109\/TSMC.1972.4309137"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63223-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T15:04:00Z","timestamp":1718895840000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63223-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031632228","9783031632235"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63223-5_7","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Corfu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}