A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era
Negative selection algorithm (NSA) is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the tra...
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| Veröffentlicht in: | Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-7 |
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| creator | Yang, Tao Li, Tao Yang, Hanli Chen, Wen Zhu, Fangdong Zhang, Fan |
| description | Negative selection algorithm (NSA) is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional “Random-Discard” model to the “Computing-Designated” model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset. |
| doi_str_mv | 10.1155/2017/3956415 |
| format | Article |
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In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional “Random-Discard” model to the “Computing-Designated” model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2017/3956415</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Antigens ; Big Data ; Classification ; Colleges & universities ; Cost analysis ; Efficiency ; Mathematical problems ; Sensors ; Voronoi graphs</subject><ispartof>Mathematical problems in engineering, 2017-01, Vol.2017 (2017), p.1-7</ispartof><rights>Copyright © 2017 Fangdong Zhu et al.</rights><rights>Copyright © 2017 Fangdong Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-4f760402634f4fac7496fd2fcf46fcb1cc2803dad469c54c6caf1e4806a393033</citedby><cites>FETCH-LOGICAL-c360t-4f760402634f4fac7496fd2fcf46fcb1cc2803dad469c54c6caf1e4806a393033</cites><orcidid>0000-0002-1525-9096 ; 0000-0002-5930-360X ; 0000-0003-0566-2495</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><contributor>Zhang, Zonghua</contributor><creatorcontrib>Yang, Tao</creatorcontrib><creatorcontrib>Li, Tao</creatorcontrib><creatorcontrib>Yang, Hanli</creatorcontrib><creatorcontrib>Chen, Wen</creatorcontrib><creatorcontrib>Zhu, Fangdong</creatorcontrib><creatorcontrib>Zhang, Fan</creatorcontrib><title>A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era</title><title>Mathematical problems in engineering</title><description>Negative selection algorithm (NSA) is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. 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The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.</description><subject>Algorithms</subject><subject>Antigens</subject><subject>Big Data</subject><subject>Classification</subject><subject>Colleges & universities</subject><subject>Cost analysis</subject><subject>Efficiency</subject><subject>Mathematical problems</subject><subject>Sensors</subject><subject>Voronoi graphs</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNqF0E1LAzEQBuAgCtbqzbMEPOraTL5291hr_QCxFBW8hZhN2tTtbk22iv_ebbfg0cvMHB7egRehUyBXAEIMKIF0wHIhOYg91AMhWSKAp_vtTShPgLK3Q3QU44IQCgKyHpoO8XTtzQd-sjPd-C-Ln21pTePrCg_LWR18M19iVwc8qWwyKnWMeDu980Zvma_wtZ_hG91oPA76GB04XUZ7stt99Ho7fhndJ4-Tu4fR8DExTJIm4S6VhBMqGXfcaZPyXLqCOuO4dOYdjKEZYYUuuMyN4EYa7cDyjEjNckYY66PzLncV6s-1jY1a1OtQtS8V5MAkk2kmWnXZKRPqGIN1ahX8UocfBURtSlOb0tSutJZfdHzuq0J_-__0Wadta6zTfxpywghlv0yUdBA</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Yang, Tao</creator><creator>Li, Tao</creator><creator>Yang, Hanli</creator><creator>Chen, Wen</creator><creator>Zhu, Fangdong</creator><creator>Zhang, Fan</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-1525-9096</orcidid><orcidid>https://orcid.org/0000-0002-5930-360X</orcidid><orcidid>https://orcid.org/0000-0003-0566-2495</orcidid></search><sort><creationdate>20170101</creationdate><title>A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era</title><author>Yang, Tao ; 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In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional “Random-Discard” model to the “Computing-Designated” model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. 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| source | Wiley Online Library Open Access; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Algorithms Antigens Big Data Classification Colleges & universities Cost analysis Efficiency Mathematical problems Sensors Voronoi graphs |
| title | A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era |
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