{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:13:31Z","timestamp":1760710411131,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789813342132"},{"type":"electronic","value":"9789813342149"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-33-4214-9_13","type":"book-chapter","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T05:26:34Z","timestamp":1606195594000},"page":"169-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Local Centroid Distance Constrained Representation-Based K-Nearest Neighbor Classifier"],"prefix":"10.1007","author":[{"given":"Yingying","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yitong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xingcheng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"En","family":"Zou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,20]]},"reference":[{"issue":"11","key":"13_CR1","doi-asserted-by":"publisher","first-page":"5185","DOI":"10.1109\/TNNLS.2018.2791507","volume":"29","author":"X Wu","year":"2018","unstructured":"Wu, X., Zuo, W., Lin, L., Jia, W., Zhang, D.: F-SVM: combination of feature transformation and SVM learning via convex relaxation. IEEE Trans. Neural Networks Learn. Syst. 29(11), 5185\u20135199 (2018)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"3","key":"13_CR2","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/21.97458","volume":"21","author":"SR Safavian","year":"1991","unstructured":"Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man, Cybern. 21(3), 660\u2013674 (1991)","journal-title":"IEEE Trans. Syst. Man, Cybern."},{"issue":"2","key":"13_CR3","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TKDE.2018.2836440","volume":"31","author":"L Jiang","year":"2019","unstructured":"Jiang, L., Zhang, L., Li, C., Wu, J.: A correlation-based feature weighting filter for Naive Bayes. IEEE Trans. Knowl. Data Eng. 31(2), 201\u2013213 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"10","key":"13_CR4","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. Theor. 13(10), 21\u201327 (1967)","journal-title":"IEEE Trans. Inf. Theor."},{"issue":"11","key":"13_CR5","doi-asserted-by":"crossref","first-page":"5713","DOI":"10.1109\/TNNLS.2018.2812279","volume":"29","author":"SS Mullick","year":"2018","unstructured":"Mullick, S.S., Datta, S., Das, S.: Adaptive learning-based $$K$$-Nearest Neighbor classifiers with resilience to class imbalance. IEEE Trans. Neural Networks Learn. Syst. 29(11), 5713\u20135725 (2018)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"2","key":"13_CR6","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1109\/TNNLS.2015.2506821","volume":"28","author":"N Garciapedrajas","year":"2017","unstructured":"Garciapedrajas, N., Castillo, J.A., Cerruelagarcia, G.: A proposal for local $$k$$ values for $$k$$-nearest neighbor rule. IEEE Trans. Neural Networks Learn. Syst. 28(2), 470\u2013475 (2017)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"5","key":"13_CR7","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","volume":"29","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Li, X., Zong, M., Zhu, X., Wang, R.: Efficient KNN classification with different numbers of nearest neighbors. IEEE Trans. Neural Networks Learn. Syst. 29(5), 1774\u20131785 (2018)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"10","key":"13_CR8","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1016\/j.patrec.2005.12.016","volume":"27","author":"Y Mitani","year":"2006","unstructured":"Mitani, Y., Hamamoto, Y.: A local mean-based nonparametric classifier. Pattern Recogn. Lett. 27(10), 1151\u20131159 (2006)","journal-title":"Pattern Recogn. Lett."},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.eswa.2016.09.031","volume":"67","author":"Z Pan","year":"2017","unstructured":"Pan, Z., Wang, Y., Ku, W.: A new $$k$$-harmonic nearest neighbor classifier based on the multi-local means. Expert Syst. Appl. 67, 115\u2013125 (2017)","journal-title":"Expert Syst. Appl."},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.eswa.2018.08.021","volume":"115","author":"J Gou","year":"2019","unstructured":"Gou, J., Ma, H., Ou, W., Zeng, S., Rao, Y., Yang, H.: A generalized mean distance-based $$K$$-nearest neighbor classifier. Expert Syst. Appl. 115, 356\u2013372 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"13_CR11","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright, J., Yang, A.Y., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210\u2013227 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR12","unstructured":"Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition?. In: Proceedings of the 2011 International Conference on Computer Vision, pp. 471\u2013478. IEEE, Barcelona (2011)"},{"issue":"2","key":"13_CR13","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.patrec.2012.09.024","volume":"34","author":"J Waqas","year":"2013","unstructured":"Waqas, J., Yi, Z., Zhang, L.: Collaborative neighbor representation based classification using l2-minimization approach. Pattern Recogn. Lett. 34(2), 201\u2013208 (2013)","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"13_CR14","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1109\/TGRS.2013.2241773","volume":"52","author":"W Li","year":"2014","unstructured":"Li, W., Tramel, E.W., Prasad, S., Fowler, J.E.: Nearest regularized subspace for hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 52(1), 477\u2013489 (2014)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.knosys.2014.07.020","volume":"70","author":"J Gou","year":"2014","unstructured":"Gou, J., Zhan, Y., Rao, Y., Shen, X., Wang, X., He, W.: Improved pseudo nearest neighbor classification. Knowl. Based Syst. 70, 361\u2013375 (2014)","journal-title":"Knowl. Based Syst."},{"issue":"3","key":"13_CR16","first-page":"29.1","volume":"10","author":"J Gou","year":"2019","unstructured":"Gou, J., Qiu, W., Yi, Z., Xu, Y., Mao, Q., Zhan, Y.: A local mean representation-based $$K$$-nearest neighbor classifier. ACM Trans. Intell. Syst. 10(3), 29.1\u201329.25 (2019)","journal-title":"ACM Trans. Intell. Syst."},{"issue":"4","key":"13_CR17","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","volume":"6","author":"MF Moller","year":"1993","unstructured":"Moller, M.F.: Original contribution: a scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525\u2013533 (1993)","journal-title":"Neural Netw."}],"container-title":["Communications in Computer and Information Science","Wireless Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-33-4214-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T12:30:52Z","timestamp":1669725052000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-33-4214-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789813342132","9789813342149"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-33-4214-9_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"20 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CWSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Conference on Wireless Sensor Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dunhuang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cwsn2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cwsn2020.aconf.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"85","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":"20","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":"24% - 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":"2","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\/5","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)"}}]}}