A comparative evaluation of novelty detection algorithms for discrete sequences
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of candidate methods for the novelty detection problem applied to discrete seq...
Gespeichert in:
| Veröffentlicht in: | The Artificial intelligence review 2020-06, Vol.53 (5), p.3787-3812 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3812 |
|---|---|
| container_issue | 5 |
| container_start_page | 3787 |
| container_title | The Artificial intelligence review |
| container_volume | 53 |
| creator | Domingues, Rémi Michiardi, Pietro Barlet, Jérémie Filippone, Maurizio |
| description | The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of candidate methods for the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods’ performance, key selection criteria to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints. |
| doi_str_mv | 10.1007/s10462-019-09779-4 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2403241759</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A718214188</galeid><sourcerecordid>A718214188</sourcerecordid><originalsourceid>FETCH-LOGICAL-c358t-bba4098c9de3d9859312e7c0e886e1eed147f00939f07f37926b2641795f2c663</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWKt_wNOC59TJx242x1L8gkIveg5pdlK3bDc12Rb6741dwZvkMOGd950ZHkLuGcwYgHpMDGTFKTBNQSulqbwgE1YqQVXWL8kEeKUprzm7JjcpbQGg5FJMyGpeuLDb22iH9ogFHm13yN_QF8EXfThiN5yKBgd0Z9F2mxDb4XOXCh9i0bTJxdwsEn4dsHeYbsmVt13Cu986JR_PT--LV7pcvbwt5kvqRFkPdL22EnTtdIOi0XWpBeOoHGBdV8gQGyaVB9BCe1BeKM2rNa8kU7r03FWVmJKHce4-hrw6DWYbDrHPKw2XIHi25qFTMhtdG9uhaXsfhmhdfg3uWhd69G3W54plMJLVdQ7wMeBiSCmiN_vY7mw8GQbmh7QZSZtM2pxJG5lDYgylbO43GP9u-Sf1DfVIgMI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2403241759</pqid></control><display><type>article</type><title>A comparative evaluation of novelty detection algorithms for discrete sequences</title><source>Springer Nature - Complete Springer Journals</source><creator>Domingues, Rémi ; Michiardi, Pietro ; Barlet, Jérémie ; Filippone, Maurizio</creator><creatorcontrib>Domingues, Rémi ; Michiardi, Pietro ; Barlet, Jérémie ; Filippone, Maurizio</creatorcontrib><description>The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of candidate methods for the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods’ performance, key selection criteria to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.</description><identifier>ISSN: 0269-2821</identifier><identifier>EISSN: 1573-7462</identifier><identifier>DOI: 10.1007/s10462-019-09779-4</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Analysis ; Anomalies ; Artificial Intelligence ; Computer Science ; Datasets ; Embedded systems ; Fraud ; Genomics ; Identification methods ; Probability ; Response time</subject><ispartof>The Artificial intelligence review, 2020-06, Vol.53 (5), p.3787-3812</ispartof><rights>Springer Nature B.V. 2019</rights><rights>COPYRIGHT 2020 Springer</rights><rights>Springer Nature B.V. 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-bba4098c9de3d9859312e7c0e886e1eed147f00939f07f37926b2641795f2c663</citedby><cites>FETCH-LOGICAL-c358t-bba4098c9de3d9859312e7c0e886e1eed147f00939f07f37926b2641795f2c663</cites><orcidid>0000-0001-9613-2187</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10462-019-09779-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10462-019-09779-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Domingues, Rémi</creatorcontrib><creatorcontrib>Michiardi, Pietro</creatorcontrib><creatorcontrib>Barlet, Jérémie</creatorcontrib><creatorcontrib>Filippone, Maurizio</creatorcontrib><title>A comparative evaluation of novelty detection algorithms for discrete sequences</title><title>The Artificial intelligence review</title><addtitle>Artif Intell Rev</addtitle><description>The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of candidate methods for the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods’ performance, key selection criteria to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Anomalies</subject><subject>Artificial Intelligence</subject><subject>Computer Science</subject><subject>Datasets</subject><subject>Embedded systems</subject><subject>Fraud</subject><subject>Genomics</subject><subject>Identification methods</subject><subject>Probability</subject><subject>Response time</subject><issn>0269-2821</issn><issn>1573-7462</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1LAzEQhoMoWKt_wNOC59TJx242x1L8gkIveg5pdlK3bDc12Rb6741dwZvkMOGd950ZHkLuGcwYgHpMDGTFKTBNQSulqbwgE1YqQVXWL8kEeKUprzm7JjcpbQGg5FJMyGpeuLDb22iH9ogFHm13yN_QF8EXfThiN5yKBgd0Z9F2mxDb4XOXCh9i0bTJxdwsEn4dsHeYbsmVt13Cu986JR_PT--LV7pcvbwt5kvqRFkPdL22EnTtdIOi0XWpBeOoHGBdV8gQGyaVB9BCe1BeKM2rNa8kU7r03FWVmJKHce4-hrw6DWYbDrHPKw2XIHi25qFTMhtdG9uhaXsfhmhdfg3uWhd69G3W54plMJLVdQ7wMeBiSCmiN_vY7mw8GQbmh7QZSZtM2pxJG5lDYgylbO43GP9u-Sf1DfVIgMI</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Domingues, Rémi</creator><creator>Michiardi, Pietro</creator><creator>Barlet, Jérémie</creator><creator>Filippone, Maurizio</creator><general>Springer Netherlands</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CNYFK</scope><scope>DWQXO</scope><scope>E3H</scope><scope>F2A</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M1O</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-9613-2187</orcidid></search><sort><creationdate>20200601</creationdate><title>A comparative evaluation of novelty detection algorithms for discrete sequences</title><author>Domingues, Rémi ; Michiardi, Pietro ; Barlet, Jérémie ; Filippone, Maurizio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c358t-bba4098c9de3d9859312e7c0e886e1eed147f00939f07f37926b2641795f2c663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Anomalies</topic><topic>Artificial Intelligence</topic><topic>Computer Science</topic><topic>Datasets</topic><topic>Embedded systems</topic><topic>Fraud</topic><topic>Genomics</topic><topic>Identification methods</topic><topic>Probability</topic><topic>Response time</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Domingues, Rémi</creatorcontrib><creatorcontrib>Michiardi, Pietro</creatorcontrib><creatorcontrib>Barlet, Jérémie</creatorcontrib><creatorcontrib>Filippone, Maurizio</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Library & Information Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Library Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><jtitle>The Artificial intelligence review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Domingues, Rémi</au><au>Michiardi, Pietro</au><au>Barlet, Jérémie</au><au>Filippone, Maurizio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparative evaluation of novelty detection algorithms for discrete sequences</atitle><jtitle>The Artificial intelligence review</jtitle><stitle>Artif Intell Rev</stitle><date>2020-06-01</date><risdate>2020</risdate><volume>53</volume><issue>5</issue><spage>3787</spage><epage>3812</epage><pages>3787-3812</pages><issn>0269-2821</issn><eissn>1573-7462</eissn><abstract>The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of candidate methods for the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods’ performance, key selection criteria to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10462-019-09779-4</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0001-9613-2187</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0269-2821 |
| ispartof | The Artificial intelligence review, 2020-06, Vol.53 (5), p.3787-3812 |
| issn | 0269-2821 1573-7462 |
| language | eng |
| recordid | cdi_proquest_journals_2403241759 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Analysis Anomalies Artificial Intelligence Computer Science Datasets Embedded systems Fraud Genomics Identification methods Probability Response time |
| title | A comparative evaluation of novelty detection algorithms for discrete sequences |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-15T12%3A43%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20comparative%20evaluation%20of%20novelty%20detection%20algorithms%20for%20discrete%20sequences&rft.jtitle=The%20Artificial%20intelligence%20review&rft.au=Domingues,%20R%C3%A9mi&rft.date=2020-06-01&rft.volume=53&rft.issue=5&rft.spage=3787&rft.epage=3812&rft.pages=3787-3812&rft.issn=0269-2821&rft.eissn=1573-7462&rft_id=info:doi/10.1007/s10462-019-09779-4&rft_dat=%3Cgale_proqu%3EA718214188%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2403241759&rft_id=info:pmid/&rft_galeid=A718214188&rfr_iscdi=true |