Tree-based classifier ensembles for early detection method of diabetes: an exploratory study View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Bayu Adhi Tama, Kyung-Hyune Rhee

ABSTRACT

Diabetes is a lifestyle-driven disease which has become a critical health issue worldwide. In this paper, we conduct an exploratory study about early detection method of diabetes mellitus using various ensemble learning techniques. Eight tree-based machine learning algorithms, i.e. classification and regression tree, decision tree (C4.5), reduced error pruning tree, random tree, naive Bayes tree, functional tree, best-first decision tree and logistic model tree are employed as a base classifier in five different ensembles, i.e. bagging, boosting, random subspace, DECORATE, and rotation forest. The performance of ensembles and base classifiers are thoroughly benchmarked on three real-world datasets in term of area under receiver operating characteristic curve metric. Finally, we assess the performance differences among the classifiers using several statistical significant tests. We contribute to the existing literature regarding an extensive benchmark of tree-based classifier ensembles for early detection method of diabetes disease. More... »

PAGES

355-370

References to SciGraph publications

  • 2014. Prediction of Diabetes Mellitus Based on Boosting Ensemble Modeling in UBIQUITOUS COMPUTING AND AMBIENT INTELLIGENCE. PERSONALISATION AND USER ADAPTED SERVICES
  • 2004-06. Functional Trees in MACHINE LEARNING
  • 2005-05. Logistic Model Trees in MACHINE LEARNING
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2016-06. Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran in INTERNATIONAL JOURNAL OF DIABETES IN DEVELOPING COUNTRIES
  • 2016. Identification of Diabetes Disease Using Committees of Neural Network-Based Classifiers in MACHINE INTELLIGENCE AND BIG DATA IN INDUSTRY
  • 2013. Global Prevalence and Future of Diabetes Mellitus in DIABETES
  • 2004-07. Fusion of appearance-based face recognition algorithms in PATTERN ANALYSIS AND APPLICATIONS
  • 1996-08. Bagging predictors in MACHINE LEARNING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10462-017-9565-3

    DOI

    http://dx.doi.org/10.1007/s10462-017-9565-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1085705694


    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": "Sriwijaya University", 
              "id": "https://www.grid.ac/institutes/grid.108126.c", 
              "name": [
                "IT Convergence and Application Engineering, Pukyong National University, (48513) Daeyon Campus, 45, Yongso-ro, Nam-Gu, Busan, Korea", 
                "Faculty of Computer Science, University of Sriwijaya Jln Raya Palembang-Prabumulih Km., 32 Ogan Ilir, Sumatera Selatan, Indonesia"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Tama", 
            "givenName": "Bayu Adhi", 
            "id": "sg:person.011062114413.35", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011062114413.35"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Pukyong National University", 
              "id": "https://www.grid.ac/institutes/grid.412576.3", 
              "name": [
                "IT Convergence and Application Engineering, Pukyong National University, (48513) Daeyon Campus, 45, Yongso-ro, Nam-Gu, Busan, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Rhee", 
            "givenName": "Kyung-Hyune", 
            "id": "sg:person.015320672576.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015320672576.40"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.jocs.2016.01.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001625158"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00058655", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002929950", 
              "https://doi.org/10.1007/bf00058655"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jcss.1997.1504", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004338842"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-30315-4_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004898382", 
              "https://doi.org/10.1007/978-3-319-30315-4_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10994-005-0466-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005110454", 
              "https://doi.org/10.1007/s10994-005-0466-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10994-005-0466-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005110454", 
              "https://doi.org/10.1007/s10994-005-0466-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/b:mach.0000027782.67192.13", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007007217", 
              "https://doi.org/10.1023/b:mach.0000027782.67192.13"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.inffus.2013.04.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007208651"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ins.2009.12.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008415958"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.patrec.2005.10.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013701558"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jbi.2015.12.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017608109"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1010933404324", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024739340", 
              "https://doi.org/10.1023/a:1010933404324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.artmed.2004.07.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025296320"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/ijhc.1987.0321", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027575637"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.diabres.2009.10.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028972981"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-13102-3_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029689615", 
              "https://doi.org/10.1007/978-3-319-13102-3_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13410-015-0374-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031584520", 
              "https://doi.org/10.1007/s13410-015-0374-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ins.2014.08.056", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035593661"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.inffus.2004.04.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038746580"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4614-5441-0_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039363514", 
              "https://doi.org/10.1007/978-1-4614-5441-0_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/089976698300017197", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053132543"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10044-004-0212-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053339125", 
              "https://doi.org/10.1007/s10044-004-0212-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/00401706.1964.10490181", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058283617"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/34.709601", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061156844"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2006.211", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061743046"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/aoms/1177731944", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064402489"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.12928/telkomnika.v9i2.699", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1064757349"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/istmet.2014.6936496", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095533768"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-03", 
        "datePublishedReg": "2019-03-01", 
        "description": "Diabetes is a lifestyle-driven disease which has become a critical health issue worldwide. In this paper, we conduct an exploratory study about early detection method of diabetes mellitus using various ensemble learning techniques. Eight tree-based machine learning algorithms, i.e. classification and regression tree, decision tree (C4.5), reduced error pruning tree, random tree, naive Bayes tree, functional tree, best-first decision tree and logistic model tree are employed as a base classifier in five different ensembles, i.e. bagging, boosting, random subspace, DECORATE, and rotation forest. The performance of ensembles and base classifiers are thoroughly benchmarked on three real-world datasets in term of area under receiver operating characteristic curve metric. Finally, we assess the performance differences among the classifiers using several statistical significant tests. We contribute to the existing literature regarding an extensive benchmark of tree-based classifier ensembles for early detection method of diabetes disease.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10462-017-9565-3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.7476320", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1126843", 
            "issn": [
              "0269-2821", 
              "1573-7462"
            ], 
            "name": "Artificial Intelligence Review", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "51"
          }
        ], 
        "name": "Tree-based classifier ensembles for early detection method of diabetes: an exploratory study", 
        "pagination": "355-370", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "24e3b7982fc9e834efea7d4da3e5baad25635b1a84a038eba93d77740051d6f7"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10462-017-9565-3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1085705694"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10462-017-9565-3", 
          "https://app.dimensions.ai/details/publication/pub.1085705694"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T14:02", 
        "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/v90/0000000371_0000000371/records_130830_00000005.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs10462-017-9565-3"
      }
    ]
     

    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/s10462-017-9565-3'

    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/s10462-017-9565-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10462-017-9565-3'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10462-017-9565-3'


     

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

    164 TRIPLES      21 PREDICATES      54 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10462-017-9565-3 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N53fea783fbc64efab6efa4daee1f18c8
    4 schema:citation sg:pub.10.1007/978-1-4614-5441-0_5
    5 sg:pub.10.1007/978-3-319-13102-3_6
    6 sg:pub.10.1007/978-3-319-30315-4_6
    7 sg:pub.10.1007/bf00058655
    8 sg:pub.10.1007/s10044-004-0212-7
    9 sg:pub.10.1007/s10994-005-0466-3
    10 sg:pub.10.1007/s13410-015-0374-4
    11 sg:pub.10.1023/a:1010933404324
    12 sg:pub.10.1023/b:mach.0000027782.67192.13
    13 https://doi.org/10.1006/ijhc.1987.0321
    14 https://doi.org/10.1006/jcss.1997.1504
    15 https://doi.org/10.1016/j.artmed.2004.07.002
    16 https://doi.org/10.1016/j.diabres.2009.10.007
    17 https://doi.org/10.1016/j.inffus.2004.04.001
    18 https://doi.org/10.1016/j.inffus.2013.04.006
    19 https://doi.org/10.1016/j.ins.2009.12.010
    20 https://doi.org/10.1016/j.ins.2014.08.056
    21 https://doi.org/10.1016/j.jbi.2015.12.001
    22 https://doi.org/10.1016/j.jocs.2016.01.001
    23 https://doi.org/10.1016/j.patrec.2005.10.010
    24 https://doi.org/10.1080/00401706.1964.10490181
    25 https://doi.org/10.1109/34.709601
    26 https://doi.org/10.1109/istmet.2014.6936496
    27 https://doi.org/10.1109/tpami.2006.211
    28 https://doi.org/10.1162/089976698300017197
    29 https://doi.org/10.1214/aoms/1177731944
    30 https://doi.org/10.12928/telkomnika.v9i2.699
    31 schema:datePublished 2019-03
    32 schema:datePublishedReg 2019-03-01
    33 schema:description Diabetes is a lifestyle-driven disease which has become a critical health issue worldwide. In this paper, we conduct an exploratory study about early detection method of diabetes mellitus using various ensemble learning techniques. Eight tree-based machine learning algorithms, i.e. classification and regression tree, decision tree (C4.5), reduced error pruning tree, random tree, naive Bayes tree, functional tree, best-first decision tree and logistic model tree are employed as a base classifier in five different ensembles, i.e. bagging, boosting, random subspace, DECORATE, and rotation forest. The performance of ensembles and base classifiers are thoroughly benchmarked on three real-world datasets in term of area under receiver operating characteristic curve metric. Finally, we assess the performance differences among the classifiers using several statistical significant tests. We contribute to the existing literature regarding an extensive benchmark of tree-based classifier ensembles for early detection method of diabetes disease.
    34 schema:genre research_article
    35 schema:inLanguage en
    36 schema:isAccessibleForFree false
    37 schema:isPartOf N05ee11a507bb4fc8a06fc7210a88c32c
    38 N42e102ae421b40b8baf788442b02cc99
    39 sg:journal.1126843
    40 schema:name Tree-based classifier ensembles for early detection method of diabetes: an exploratory study
    41 schema:pagination 355-370
    42 schema:productId N4604eade720843bcade18bdf8194cfed
    43 N4b2de7fdf3314d77aa6037637c26b3e0
    44 N670a0354a8d84f9ca4f27c1a61a23b39
    45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085705694
    46 https://doi.org/10.1007/s10462-017-9565-3
    47 schema:sdDatePublished 2019-04-11T14:02
    48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    49 schema:sdPublisher Nb15cd8278f9342169b583fb98481bbf7
    50 schema:url https://link.springer.com/10.1007%2Fs10462-017-9565-3
    51 sgo:license sg:explorer/license/
    52 sgo:sdDataset articles
    53 rdf:type schema:ScholarlyArticle
    54 N05ee11a507bb4fc8a06fc7210a88c32c schema:issueNumber 3
    55 rdf:type schema:PublicationIssue
    56 N42e102ae421b40b8baf788442b02cc99 schema:volumeNumber 51
    57 rdf:type schema:PublicationVolume
    58 N4604eade720843bcade18bdf8194cfed schema:name readcube_id
    59 schema:value 24e3b7982fc9e834efea7d4da3e5baad25635b1a84a038eba93d77740051d6f7
    60 rdf:type schema:PropertyValue
    61 N4b2de7fdf3314d77aa6037637c26b3e0 schema:name dimensions_id
    62 schema:value pub.1085705694
    63 rdf:type schema:PropertyValue
    64 N53fea783fbc64efab6efa4daee1f18c8 rdf:first sg:person.011062114413.35
    65 rdf:rest N570645f6965445ca9cf1b2ed837381e5
    66 N570645f6965445ca9cf1b2ed837381e5 rdf:first sg:person.015320672576.40
    67 rdf:rest rdf:nil
    68 N670a0354a8d84f9ca4f27c1a61a23b39 schema:name doi
    69 schema:value 10.1007/s10462-017-9565-3
    70 rdf:type schema:PropertyValue
    71 Nb15cd8278f9342169b583fb98481bbf7 schema:name Springer Nature - SN SciGraph project
    72 rdf:type schema:Organization
    73 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    74 schema:name Information and Computing Sciences
    75 rdf:type schema:DefinedTerm
    76 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    77 schema:name Artificial Intelligence and Image Processing
    78 rdf:type schema:DefinedTerm
    79 sg:grant.7476320 http://pending.schema.org/fundedItem sg:pub.10.1007/s10462-017-9565-3
    80 rdf:type schema:MonetaryGrant
    81 sg:journal.1126843 schema:issn 0269-2821
    82 1573-7462
    83 schema:name Artificial Intelligence Review
    84 rdf:type schema:Periodical
    85 sg:person.011062114413.35 schema:affiliation https://www.grid.ac/institutes/grid.108126.c
    86 schema:familyName Tama
    87 schema:givenName Bayu Adhi
    88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011062114413.35
    89 rdf:type schema:Person
    90 sg:person.015320672576.40 schema:affiliation https://www.grid.ac/institutes/grid.412576.3
    91 schema:familyName Rhee
    92 schema:givenName Kyung-Hyune
    93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015320672576.40
    94 rdf:type schema:Person
    95 sg:pub.10.1007/978-1-4614-5441-0_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039363514
    96 https://doi.org/10.1007/978-1-4614-5441-0_5
    97 rdf:type schema:CreativeWork
    98 sg:pub.10.1007/978-3-319-13102-3_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029689615
    99 https://doi.org/10.1007/978-3-319-13102-3_6
    100 rdf:type schema:CreativeWork
    101 sg:pub.10.1007/978-3-319-30315-4_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004898382
    102 https://doi.org/10.1007/978-3-319-30315-4_6
    103 rdf:type schema:CreativeWork
    104 sg:pub.10.1007/bf00058655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002929950
    105 https://doi.org/10.1007/bf00058655
    106 rdf:type schema:CreativeWork
    107 sg:pub.10.1007/s10044-004-0212-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053339125
    108 https://doi.org/10.1007/s10044-004-0212-7
    109 rdf:type schema:CreativeWork
    110 sg:pub.10.1007/s10994-005-0466-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005110454
    111 https://doi.org/10.1007/s10994-005-0466-3
    112 rdf:type schema:CreativeWork
    113 sg:pub.10.1007/s13410-015-0374-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031584520
    114 https://doi.org/10.1007/s13410-015-0374-4
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
    117 https://doi.org/10.1023/a:1010933404324
    118 rdf:type schema:CreativeWork
    119 sg:pub.10.1023/b:mach.0000027782.67192.13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007007217
    120 https://doi.org/10.1023/b:mach.0000027782.67192.13
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1006/ijhc.1987.0321 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027575637
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1006/jcss.1997.1504 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004338842
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1016/j.artmed.2004.07.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025296320
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1016/j.diabres.2009.10.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028972981
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1016/j.inffus.2004.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038746580
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1016/j.inffus.2013.04.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007208651
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1016/j.ins.2009.12.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008415958
    135 rdf:type schema:CreativeWork
    136 https://doi.org/10.1016/j.ins.2014.08.056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035593661
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1016/j.jbi.2015.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017608109
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1016/j.jocs.2016.01.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001625158
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1016/j.patrec.2005.10.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013701558
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1080/00401706.1964.10490181 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058283617
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1109/34.709601 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156844
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1109/istmet.2014.6936496 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095533768
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1109/tpami.2006.211 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743046
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1162/089976698300017197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053132543
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1214/aoms/1177731944 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064402489
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.12928/telkomnika.v9i2.699 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064757349
    157 rdf:type schema:CreativeWork
    158 https://www.grid.ac/institutes/grid.108126.c schema:alternateName Sriwijaya University
    159 schema:name Faculty of Computer Science, University of Sriwijaya Jln Raya Palembang-Prabumulih Km., 32 Ogan Ilir, Sumatera Selatan, Indonesia
    160 IT Convergence and Application Engineering, Pukyong National University, (48513) Daeyon Campus, 45, Yongso-ro, Nam-Gu, Busan, Korea
    161 rdf:type schema:Organization
    162 https://www.grid.ac/institutes/grid.412576.3 schema:alternateName Pukyong National University
    163 schema:name IT Convergence and Application Engineering, Pukyong National University, (48513) Daeyon Campus, 45, Yongso-ro, Nam-Gu, Busan, Korea
    164 rdf:type schema:Organization
     




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


    ...