{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T14:54:02Z","timestamp":1773500042450,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY19F020025"],"award-info":[{"award-number":["LY19F020025"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Major Special Funding for \u201cScience and Technology Innovation 2025\u201d in Ningbo","award":["2018B10063"],"award-info":[{"award-number":["2018B10063"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072406"],"award-info":[{"award-number":["62072406"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s10489-020-02001-x","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:03:13Z","timestamp":1609718593000},"page":"4525-4547","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A fast detector generation algorithm for negative selection"],"prefix":"10.1007","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7153-2755","authenticated-orcid":false,"given":"Jinyin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueke","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengmeng","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"2001_CR1","doi-asserted-by":"crossref","unstructured":"Silva G C, Dasgupta D (2016) A survey of recent works in artificial immune systems. In: Handbook on computational intelligence: vol 2: evolutionary computation, hybrid systems, and applications, pp 547\u2014586","DOI":"10.1142\/9789814675017_0015"},{"key":"2001_CR2","doi-asserted-by":"crossref","unstructured":"Zareen F, Karam R (2018) Detecting RTL trojans using artificial immune systems and high level behavior classification. In: 2018 Asian hardware oriented security and trust symposium (AsianHOST). IEEE, pp 68\u201373","DOI":"10.1109\/AsianHOST.2018.8607172"},{"key":"2001_CR3","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.eswa.2016.03.042","volume":"60","author":"P Saurabh","year":"2016","unstructured":"Saurabh P, Verma B (2016) An efficient proactive artificial immune system based anomaly detection and prevention system. Expert Syst Appl 60:311\u2013320","journal-title":"Expert Syst Appl"},{"key":"2001_CR4","doi-asserted-by":"crossref","unstructured":"Zhao L, Zhou L, Dai Y, et al. (2015) Artificial immune system used in rotating machinery fault diagnosis. In: Proceedings of the 2015 Chinese intelligent automation conference. Springer, Berlin, pp 65\u201374","DOI":"10.1007\/978-3-662-46463-2_8"},{"issue":"2","key":"2001_CR5","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s10115-013-0642-x","volume":"40","author":"X Zhao","year":"2014","unstructured":"Zhao X, Wen Z, Li X. (2014) Qos-aware web service selection with negative selection algorithm. Knowl Inf Syst 40(2):349\u2013373","journal-title":"Knowl Inf Syst"},{"key":"2001_CR6","doi-asserted-by":"crossref","unstructured":"Kumar G V P, Reddy D K (2014) An agent based intrusion detection system for wireless network with artificial immune system (AIS) and negative clone selection. In: 2014 International conference on electronic systems, signal processing and computing technologies. IEEE, pp 429\u2013433","DOI":"10.1109\/ICESC.2014.73"},{"issue":"1","key":"2001_CR7","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s00521-013-1447-2","volume":"25","author":"XZ Gao","year":"2014","unstructured":"Gao X Z, Wang X, Zenger K (2014) Motor fault diagnosis using negative selection algorithm. Neural Comput & Applic 25(1):55\u201365","journal-title":"Neural Comput & Applic"},{"key":"2001_CR8","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.aeue.2016.12.004","volume":"72","author":"CCE de Abreu","year":"2017","unstructured":"de Abreu C C E, Duarte M A Q, Villarreal F (2017) An immunological approach based on the negative selection algorithm for real noise classification in speech signals. AEU Int J Electron Commun 72:125\u2013133","journal-title":"AEU Int J Electron Commun"},{"key":"2001_CR9","doi-asserted-by":"publisher","first-page":"44967","DOI":"10.1109\/ACCESS.2020.2976875","volume":"8","author":"C Yang","year":"2020","unstructured":"Yang C, Jia L, Chen B Q, et al. (2020) Negative selection algorithm based on antigen density clustering. IEEE Access 8:44967\u201344975","journal-title":"IEEE Access"},{"issue":"14","key":"2001_CR10","doi-asserted-by":"publisher","first-page":"e3955","DOI":"10.1002\/cpe.3955","volume":"29","author":"B Li","year":"2017","unstructured":"Li B, Zhang S, Li K (2017) Towards a multi-layers anomaly detection framework for analyzing network traffic. Concurrency and Computation: Practice and Experience 29(14):e3955","journal-title":"Concurrency and Computation: Practice and Experience"},{"issue":"10","key":"2001_CR11","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1016\/j.ins.2008.12.015","volume":"179","author":"Z Ji","year":"2009","unstructured":"Ji Z, Dasgupta D (2009) V-detector: an efficient negative selection algorithm with \u2018probably adequate\u2019 detector coverage. Inf Sci 179(10):1390\u20131406","journal-title":"Inf Sci"},{"key":"2001_CR12","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.knosys.2012.01.004","volume":"30","author":"M Gong","year":"2012","unstructured":"Gong M, Zhang J, Ma J, Jiao L (2012) An efficient negative selection algorithm with further training for anomaly detection. Knowl-Based Syst 30:185\u2013191","journal-title":"Knowl-Based Syst"},{"key":"2001_CR13","doi-asserted-by":"crossref","unstructured":"Li D, Liu S, Zhang H, et al. (2015) Negative selection algorithm with constant detectors for anomaly detection. Appl Soft Comput 618\u2013632","DOI":"10.1016\/j.asoc.2015.08.011"},{"key":"2001_CR14","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.engappai.2013.12.001","volume":"28","author":"I Idris","year":"2014","unstructured":"Idris I, Selamat A, Omatu S (2014) Hybrid email spam detection model with negative selection algorithm and differential evolution. Eng Appl Artif Intell 28:97\u2013110","journal-title":"Eng Appl Artif Intell"},{"key":"2001_CR15","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.patcog.2016.11.026","volume":"64","author":"LI Dong","year":"2017","unstructured":"Dong L I, Shulin L I U, Zhang H (2017) A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples. Pattern Recogn 64:374\u2013385","journal-title":"Pattern Recogn"},{"key":"2001_CR16","doi-asserted-by":"crossref","unstructured":"Kimura N, Takeda Y, Hasegawa T, et al. (2017) Agent based fault detection using negative selection algorithm for chemical processes. In: 2017 6th international symposium on advanced control of industrial processes (AdCONIP). IEEE, pp 448\u2013452","DOI":"10.1109\/ADCONIP.2017.7983822"},{"issue":"2","key":"2001_CR17","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s10489-014-0599-9","volume":"42","author":"X Xiao","year":"2015","unstructured":"Xiao X, Li T, Zhang R (2015) An immune optimization based real-valued negative selection algorithm. Appl Intell 42(2):289\u2013302","journal-title":"Appl Intell"},{"key":"2001_CR18","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.engappai.2016.08.014","volume":"62","author":"S Fouladvand","year":"2017","unstructured":"Fouladvand S, Osareh A, Shadgar B, et al. (2017) DENSA: an effective negative selection algorithm with flexible boundaries for self-space and dynamic number of detectors. Eng Appl Artif Intell 62:359\u2013372","journal-title":"Eng Appl Artif Intell"},{"key":"2001_CR19","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.engappai.2014.11.001","volume":"39","author":"A I Idris","year":"2015","unstructured":"I Idris A, Selamat N T, Nguyen S, Omatu O, Krejcar K, Kuca M, Penhaker A (2015) Combined negative selection algorithm\u2013particle swarm optimization for an email spam detection system. Eng Appl Artif Intell 39:33\u201344","journal-title":"Eng Appl Artif Intell"},{"issue":"1\u20132","key":"2001_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJHPCN.2016.074653","volume":"9","author":"T Wen","year":"2016","unstructured":"Wen T, Xu A, Tang J (2016) Study on extension negative selection algorithm. International Journal of High Performance Computing and Networking 9(1\u20132):1\u20137","journal-title":"International Journal of High Performance Computing and Networking"},{"issue":"1","key":"2001_CR21","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1515\/phys-2017-0013","volume":"15","author":"T Yang","year":"2017","unstructured":"Yang T, Chen W, Li T (2017) A real negative selection algorithm with evolutionary preference for anomaly detection. Open Physics 15(1):121\u2013134","journal-title":"Open Physics"},{"key":"2001_CR22","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.asoc.2017.04.031","volume":"57","author":"C Jinyin","year":"2017","unstructured":"Jinyin C, Xiang L, Haibing Z, et al. (2017) A novel cluster center fast determination clustering algorithm. Appl Soft Comput 57:539\u2013555","journal-title":"Appl Soft Comput"},{"key":"2001_CR23","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.ins.2017.08.062","volume":"420","author":"C Wen","year":"2017","unstructured":"Wen C, Tao L (2017) Parameter analysis of negative selection algorithm. Inf Sci 420:218\u2013234","journal-title":"Inf Sci"},{"key":"2001_CR24","doi-asserted-by":"publisher","first-page":"51886","DOI":"10.1109\/ACCESS.2019.2911660","volume":"7","author":"Z Fan","year":"2019","unstructured":"Fan Z, Wen C, Tao L, et al. (2019) An antigen space triangulation coverage based real-value negative selection algorithm. IEEE Access 7:51886\u201351898","journal-title":"IEEE Access"},{"issue":"1","key":"2001_CR25","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s10479-012-1238-7","volume":"216","author":"HS Park","year":"2014","unstructured":"Park H S, Lee J, Jun C H (2014) Clustering noise-included data by controlling decision errors. Ann Oper Res 216(1):129\u2013144","journal-title":"Ann Oper Res"},{"issue":"6","key":"2001_CR26","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1177\/0962280215609948","volume":"26","author":"XF Wang","year":"2017","unstructured":"Wang X F, Xu Y (2017) Fast clustering using adaptive density peak detection. Stat Methods Med Res 26(6):2800\u20132811","journal-title":"Stat Methods Med Res"},{"key":"2001_CR27","unstructured":"Liu M, Haffari G, Buntine W, et al. (2017) Leveraging linguistic resources for improving neural text classification. In: Proceedings of the Australasian language technology association workshop 2017, pp 34\u201342"},{"key":"2001_CR28","doi-asserted-by":"crossref","unstructured":"Aliahmadipour L, Torra V, Eslami E (2017) On hesitant fuzzy clustering and clustering of hesitant fuzzy data. In: Fuzzy sets, rough sets, multisets and clustering. Springer, Cham, pp 157\u2013168","DOI":"10.1007\/978-3-319-47557-8_10"},{"key":"2001_CR29","doi-asserted-by":"crossref","unstructured":"For Example, income (float), Marital Status, Forth, And So, Fast Density Clustering Algorithm for Numerical Data and Categorical Data, Mathematical Problems in Engineering, 2017, 1\u201315","DOI":"10.1155\/2017\/6393652"},{"key":"2001_CR30","unstructured":"Wang S, Wang D, Li C, et al. (2015) Comment on \u201cClustering by fast search and find of density peaks\u201d. arXiv:1501.04267"},{"issue":"2","key":"2001_CR31","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1007\/s10586-017-0859-7","volume":"20","author":"S Zhang","year":"2017","unstructured":"Zhang S, Wang H, Huang W (2017) Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust Comput 20(2):1517\u20131525","journal-title":"Clust Comput"},{"key":"2001_CR32","doi-asserted-by":"publisher","first-page":"6191","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez A, Laio A (2014) Machine learning. Clustering by fast search and find of density peaks. Science 344:6191","journal-title":"Science"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-02001-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-02001-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-02001-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T08:40:53Z","timestamp":1624005653000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-02001-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":32,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["2001"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-02001-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"1 October 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}