An adaptive algorithm for anomaly and novelty detection in evolving data streams View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-11

AUTHORS

Mohamed-Rafik Bouguelia, Slawomir Nowaczyk, Amir H. Payberah

ABSTRACT

In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribution, several complex environments deal with data streams that are subject to change over time. Taking this aspect into consideration is an important step towards building truly aware and intelligent systems. In this paper, we propose GNG-A, an adaptive method for incremental unsupervised learning from evolving data streams experiencing various types of change. The proposed method maintains a continuously updated network (graph) of neurons by extending the Growing Neural Gas algorithm with three complementary mechanisms, allowing it to closely track both gradual and sudden changes in the data distribution. First, an adaptation mechanism handles local changes where the distribution is only non-stationary in some regions of the feature space. Second, an adaptive forgetting mechanism identifies and removes neurons that become irrelevant due to the evolving nature of the stream. Finally, a probabilistic evolution mechanism creates new neurons when there is a need to represent data in new regions of the feature space. The proposed method is demonstrated for anomaly and novelty detection in non-stationary environments. Results show that the method handles different data distributions and efficiently reacts to various types of change. More... »

PAGES

1597-1633

References to SciGraph publications

  • 2016. An Overview of Concept Drift Applications in BIG DATA ANALYSIS: NEW ALGORITHMS FOR A NEW SOCIETY
  • 1997. A self-organizing network that can follow non-stationary distributions in ARTIFICIAL NEURAL NETWORKS — ICANN'97
  • 2015-12. One-class classifiers with incremental learning and forgetting for data streams with concept drift in SOFT COMPUTING
  • 2010-03. Tracking recurring contexts using ensemble classifiers: an application to email filtering in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2004. Learning with Drift Detection in ADVANCES IN ARTIFICIAL INTELLIGENCE – SBIA 2004
  • 2016-07. Characterizing concept drift in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2001-03. Machine Learning for User Modeling in USER MODELING AND USER-ADAPTED INTERACTION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10618-018-0571-0

    DOI

    http://dx.doi.org/10.1007/s10618-018-0571-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1103962869


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Halmstad University", 
              "id": "https://www.grid.ac/institutes/grid.73638.39", 
              "name": [
                "Center for Applied Intelligent Systems Research, Halmstad University, 30118, Halmstad, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bouguelia", 
            "givenName": "Mohamed-Rafik", 
            "id": "sg:person.014137174774.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014137174774.36"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Halmstad University", 
              "id": "https://www.grid.ac/institutes/grid.73638.39", 
              "name": [
                "Center for Applied Intelligent Systems Research, Halmstad University, 30118, Halmstad, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Nowaczyk", 
            "givenName": "Slawomir", 
            "id": "sg:person.014751544463.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014751544463.52"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Swedish Institute of Computer Science", 
              "id": "https://www.grid.ac/institutes/grid.6383.e", 
              "name": [
                "Swedish Institute of Computer Science, Stockholm, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Payberah", 
            "givenName": "Amir H.", 
            "id": "sg:person.014017373647.81", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014017373647.81"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1109/npc.2008.81", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000641091"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bfb0020222", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001583926", 
              "https://doi.org/10.1007/bfb0020222"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.patrec.2013.02.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004122267"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.procs.2015.07.322", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004477108"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-26989-4_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010273936", 
              "https://doi.org/10.1007/978-3-319-26989-4_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2007.12.024", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011033329"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0925-2312(98)00030-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011710741"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/502512.502568", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012197181"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10618-015-0448-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015955899", 
              "https://doi.org/10.1007/s10618-015-0448-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00500-014-1492-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017597109", 
              "https://doi.org/10.1007/s00500-014-1492-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00500-014-1492-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017597109", 
              "https://doi.org/10.1007/s00500-014-1492-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2523813", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018349690"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/089976699300016890", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021170494"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10115-009-0206-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023625331", 
              "https://doi.org/10.1007/s10115-009-0206-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10115-009-0206-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023625331", 
              "https://doi.org/10.1007/s10115-009-0206-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10115-009-0206-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023625331", 
              "https://doi.org/10.1007/s10115-009-0206-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0168-1699(99)00046-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025567172"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.procs.2016.05.438", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034597736"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1011117102175", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039785209", 
              "https://doi.org/10.1023/a:1011117102175"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neunet.2014.08.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039947794"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0893-6080(02)00078-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040951771"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0893-6080(02)00078-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040951771"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2480362.2480516", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043689689"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3182/20130902-3-cn-3020.00044", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044738405"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1557019.1557060", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044890580"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.engappai.2011.03.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048948660"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2012.10.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050955514"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-28645-5_29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051660541", 
              "https://doi.org/10.1007/978-3-540-28645-5_29"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-28645-5_29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051660541", 
              "https://doi.org/10.1007/978-3-540-28645-5_29"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-012088469-8.50019-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053208619"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/72.238311", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061218370"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2012.136", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061662531"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tnn.2011.2160459", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061717915"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tnnls.2013.2251352", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061718283"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tsmc.2016.2585566", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061794654"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.15265/iys-2016-s034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067702220"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.15265/iys-2016-s034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1067702220"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9781611974010.98", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1088797513"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/1.9781611972771.42", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1088800201"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ijcnn.2015.7280610", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094038895"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdm.2010.160", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094071513"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ijcnn.2005.1556026", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094931814"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdm.2016.0040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094947801"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iceceng.2011.6057677", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094958546"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icsmc.2008.4811799", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095057238"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iadcc.2014.6779381", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095103145"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icdm.2008.17", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095224840"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2018-11", 
        "datePublishedReg": "2018-11-01", 
        "description": "In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribution, several complex environments deal with data streams that are subject to change over time. Taking this aspect into consideration is an important step towards building truly aware and intelligent systems. In this paper, we propose GNG-A, an adaptive method for incremental unsupervised learning from evolving data streams experiencing various types of change. The proposed method maintains a continuously updated network (graph) of neurons by extending the Growing Neural Gas algorithm with three complementary mechanisms, allowing it to closely track both gradual and sudden changes in the data distribution. First, an adaptation mechanism handles local changes where the distribution is only non-stationary in some regions of the feature space. Second, an adaptive forgetting mechanism identifies and removes neurons that become irrelevant due to the evolving nature of the stream. Finally, a probabilistic evolution mechanism creates new neurons when there is a need to represent data in new regions of the feature space. The proposed method is demonstrated for anomaly and novelty detection in non-stationary environments. Results show that the method handles different data distributions and efficiently reacts to various types of change.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10618-018-0571-0", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1041853", 
            "issn": [
              "1384-5810", 
              "1573-756X"
            ], 
            "name": "Data Mining and Knowledge Discovery", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "6", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "32"
          }
        ], 
        "name": "An adaptive algorithm for anomaly and novelty detection in evolving data streams", 
        "pagination": "1597-1633", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "9a55f15b9c3a565d92a9d1c14ae5c8ff3eef0d624604ebe667dcca93546c53ca"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10618-018-0571-0"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1103962869"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10618-018-0571-0", 
          "https://app.dimensions.ai/details/publication/pub.1103962869"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T21:47", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8687_00000568.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs10618-018-0571-0"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10618-018-0571-0'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10618-018-0571-0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10618-018-0571-0'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10618-018-0571-0'


     

    This table displays all metadata directly associated to this object as RDF triples.

    208 TRIPLES      21 PREDICATES      68 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10618-018-0571-0 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N8a843ef8c0b74d208064cd92d3e41f9f
    4 schema:citation sg:pub.10.1007/978-3-319-26989-4_4
    5 sg:pub.10.1007/978-3-540-28645-5_29
    6 sg:pub.10.1007/bfb0020222
    7 sg:pub.10.1007/s00500-014-1492-5
    8 sg:pub.10.1007/s10115-009-0206-2
    9 sg:pub.10.1007/s10618-015-0448-4
    10 sg:pub.10.1023/a:1011117102175
    11 https://doi.org/10.1016/b978-012088469-8.50019-x
    12 https://doi.org/10.1016/j.engappai.2011.03.002
    13 https://doi.org/10.1016/j.neucom.2007.12.024
    14 https://doi.org/10.1016/j.neucom.2012.10.003
    15 https://doi.org/10.1016/j.neunet.2014.08.014
    16 https://doi.org/10.1016/j.patrec.2013.02.005
    17 https://doi.org/10.1016/j.procs.2015.07.322
    18 https://doi.org/10.1016/j.procs.2016.05.438
    19 https://doi.org/10.1016/s0168-1699(99)00046-0
    20 https://doi.org/10.1016/s0893-6080(02)00078-3
    21 https://doi.org/10.1016/s0925-2312(98)00030-7
    22 https://doi.org/10.1109/72.238311
    23 https://doi.org/10.1109/iadcc.2014.6779381
    24 https://doi.org/10.1109/icdm.2008.17
    25 https://doi.org/10.1109/icdm.2010.160
    26 https://doi.org/10.1109/icdm.2016.0040
    27 https://doi.org/10.1109/iceceng.2011.6057677
    28 https://doi.org/10.1109/icsmc.2008.4811799
    29 https://doi.org/10.1109/ijcnn.2005.1556026
    30 https://doi.org/10.1109/ijcnn.2015.7280610
    31 https://doi.org/10.1109/npc.2008.81
    32 https://doi.org/10.1109/tkde.2012.136
    33 https://doi.org/10.1109/tnn.2011.2160459
    34 https://doi.org/10.1109/tnnls.2013.2251352
    35 https://doi.org/10.1109/tsmc.2016.2585566
    36 https://doi.org/10.1137/1.9781611972771.42
    37 https://doi.org/10.1137/1.9781611974010.98
    38 https://doi.org/10.1145/1557019.1557060
    39 https://doi.org/10.1145/2480362.2480516
    40 https://doi.org/10.1145/2523813
    41 https://doi.org/10.1145/502512.502568
    42 https://doi.org/10.1162/089976699300016890
    43 https://doi.org/10.15265/iys-2016-s034
    44 https://doi.org/10.3182/20130902-3-cn-3020.00044
    45 schema:datePublished 2018-11
    46 schema:datePublishedReg 2018-11-01
    47 schema:description In the era of big data, considerable research focus is being put on designing efficient algorithms capable of learning and extracting high-level knowledge from ubiquitous data streams in an online fashion. While, most existing algorithms assume that data samples are drawn from a stationary distribution, several complex environments deal with data streams that are subject to change over time. Taking this aspect into consideration is an important step towards building truly aware and intelligent systems. In this paper, we propose GNG-A, an adaptive method for incremental unsupervised learning from evolving data streams experiencing various types of change. The proposed method maintains a continuously updated network (graph) of neurons by extending the Growing Neural Gas algorithm with three complementary mechanisms, allowing it to closely track both gradual and sudden changes in the data distribution. First, an adaptation mechanism handles local changes where the distribution is only non-stationary in some regions of the feature space. Second, an adaptive forgetting mechanism identifies and removes neurons that become irrelevant due to the evolving nature of the stream. Finally, a probabilistic evolution mechanism creates new neurons when there is a need to represent data in new regions of the feature space. The proposed method is demonstrated for anomaly and novelty detection in non-stationary environments. Results show that the method handles different data distributions and efficiently reacts to various types of change.
    48 schema:genre research_article
    49 schema:inLanguage en
    50 schema:isAccessibleForFree true
    51 schema:isPartOf N11ad05b929d049b0b90ef2bc6db268c4
    52 N57033fd711bd4acf9cdf6833b59a9805
    53 sg:journal.1041853
    54 schema:name An adaptive algorithm for anomaly and novelty detection in evolving data streams
    55 schema:pagination 1597-1633
    56 schema:productId N52c55460bccf46a08e546739b336f9aa
    57 Nb5ed3bfbdfd14a8cbf1dd7215a29e348
    58 Nfc4883541af14916a96c88b29897b95c
    59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103962869
    60 https://doi.org/10.1007/s10618-018-0571-0
    61 schema:sdDatePublished 2019-04-10T21:47
    62 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    63 schema:sdPublisher N0368b90da8fb4502a86bf06daef79bb6
    64 schema:url https://link.springer.com/10.1007%2Fs10618-018-0571-0
    65 sgo:license sg:explorer/license/
    66 sgo:sdDataset articles
    67 rdf:type schema:ScholarlyArticle
    68 N0368b90da8fb4502a86bf06daef79bb6 schema:name Springer Nature - SN SciGraph project
    69 rdf:type schema:Organization
    70 N11ad05b929d049b0b90ef2bc6db268c4 schema:volumeNumber 32
    71 rdf:type schema:PublicationVolume
    72 N52c55460bccf46a08e546739b336f9aa schema:name doi
    73 schema:value 10.1007/s10618-018-0571-0
    74 rdf:type schema:PropertyValue
    75 N57033fd711bd4acf9cdf6833b59a9805 schema:issueNumber 6
    76 rdf:type schema:PublicationIssue
    77 N8a843ef8c0b74d208064cd92d3e41f9f rdf:first sg:person.014137174774.36
    78 rdf:rest Nf4214fa7c48043c4b0574d6b58c9c2e5
    79 Nb5ed3bfbdfd14a8cbf1dd7215a29e348 schema:name dimensions_id
    80 schema:value pub.1103962869
    81 rdf:type schema:PropertyValue
    82 Ndc517b7012d247af9ccd74165a7924b0 rdf:first sg:person.014017373647.81
    83 rdf:rest rdf:nil
    84 Nf4214fa7c48043c4b0574d6b58c9c2e5 rdf:first sg:person.014751544463.52
    85 rdf:rest Ndc517b7012d247af9ccd74165a7924b0
    86 Nfc4883541af14916a96c88b29897b95c schema:name readcube_id
    87 schema:value 9a55f15b9c3a565d92a9d1c14ae5c8ff3eef0d624604ebe667dcca93546c53ca
    88 rdf:type schema:PropertyValue
    89 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    90 schema:name Information and Computing Sciences
    91 rdf:type schema:DefinedTerm
    92 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    93 schema:name Artificial Intelligence and Image Processing
    94 rdf:type schema:DefinedTerm
    95 sg:journal.1041853 schema:issn 1384-5810
    96 1573-756X
    97 schema:name Data Mining and Knowledge Discovery
    98 rdf:type schema:Periodical
    99 sg:person.014017373647.81 schema:affiliation https://www.grid.ac/institutes/grid.6383.e
    100 schema:familyName Payberah
    101 schema:givenName Amir H.
    102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014017373647.81
    103 rdf:type schema:Person
    104 sg:person.014137174774.36 schema:affiliation https://www.grid.ac/institutes/grid.73638.39
    105 schema:familyName Bouguelia
    106 schema:givenName Mohamed-Rafik
    107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014137174774.36
    108 rdf:type schema:Person
    109 sg:person.014751544463.52 schema:affiliation https://www.grid.ac/institutes/grid.73638.39
    110 schema:familyName Nowaczyk
    111 schema:givenName Slawomir
    112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014751544463.52
    113 rdf:type schema:Person
    114 sg:pub.10.1007/978-3-319-26989-4_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010273936
    115 https://doi.org/10.1007/978-3-319-26989-4_4
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/978-3-540-28645-5_29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051660541
    118 https://doi.org/10.1007/978-3-540-28645-5_29
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/bfb0020222 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001583926
    121 https://doi.org/10.1007/bfb0020222
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/s00500-014-1492-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017597109
    124 https://doi.org/10.1007/s00500-014-1492-5
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1007/s10115-009-0206-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023625331
    127 https://doi.org/10.1007/s10115-009-0206-2
    128 rdf:type schema:CreativeWork
    129 sg:pub.10.1007/s10618-015-0448-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015955899
    130 https://doi.org/10.1007/s10618-015-0448-4
    131 rdf:type schema:CreativeWork
    132 sg:pub.10.1023/a:1011117102175 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039785209
    133 https://doi.org/10.1023/a:1011117102175
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1016/b978-012088469-8.50019-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1053208619
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1016/j.engappai.2011.03.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048948660
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1016/j.neucom.2007.12.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011033329
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1016/j.neucom.2012.10.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050955514
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1016/j.neunet.2014.08.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039947794
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1016/j.patrec.2013.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004122267
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1016/j.procs.2015.07.322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004477108
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1016/j.procs.2016.05.438 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034597736
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1016/s0168-1699(99)00046-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025567172
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1016/s0893-6080(02)00078-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040951771
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1016/s0925-2312(98)00030-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011710741
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1109/72.238311 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061218370
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1109/iadcc.2014.6779381 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095103145
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1109/icdm.2008.17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095224840
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1109/icdm.2010.160 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094071513
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1109/icdm.2016.0040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094947801
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1109/iceceng.2011.6057677 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094958546
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1109/icsmc.2008.4811799 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095057238
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1109/ijcnn.2005.1556026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094931814
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1109/ijcnn.2015.7280610 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094038895
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1109/npc.2008.81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000641091
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1109/tkde.2012.136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662531
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1109/tnn.2011.2160459 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717915
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1109/tnnls.2013.2251352 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061718283
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1109/tsmc.2016.2585566 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061794654
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.1137/1.9781611972771.42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088800201
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.1137/1.9781611974010.98 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088797513
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1145/1557019.1557060 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044890580
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1145/2480362.2480516 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043689689
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1145/2523813 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018349690
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1145/502512.502568 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012197181
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1162/089976699300016890 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021170494
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.15265/iys-2016-s034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067702220
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.3182/20130902-3-cn-3020.00044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044738405
    202 rdf:type schema:CreativeWork
    203 https://www.grid.ac/institutes/grid.6383.e schema:alternateName Swedish Institute of Computer Science
    204 schema:name Swedish Institute of Computer Science, Stockholm, Sweden
    205 rdf:type schema:Organization
    206 https://www.grid.ac/institutes/grid.73638.39 schema:alternateName Halmstad University
    207 schema:name Center for Applied Intelligent Systems Research, Halmstad University, 30118, Halmstad, Sweden
    208 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...