Bagging predictors View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1996-08

AUTHORS

Leo Breiman

ABSTRACT

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy. More... »

PAGES

123-140

Journal

TITLE

Machine Learning

ISSUE

2

VOLUME

24

Related Patents

  • System And Software For Creation And Modification Of Software
  • Detecting Return-Oriented Programming Payloads By Evaluating Data For A Gadget Address Space Address And Determining Whether Operations Associated With Instructions Beginning At The Address Indicate A Return-Oriented Programming Payload
  • Method And System To Safely Guide Interventions In Procedures The Substrate Whereof Is Neuronal Plasticity
  • Traffic Simulation To Identify Malicious Activity
  • Systems, Methods, And Media Protecting A Digital Data Processing Device From Attack
  • Methods, Media, And Systems For Detecting An Anomalous Sequence Of Function Calls
  • Systems, Methods, And Media For Generating Sanitized Data, Sanitizing Anomaly Detection Models, And/Or Generating Sanitized Anomaly Detection Models
  • Systems And Methods For Distributed Rules Processing
  • Systems, Methods, And Media For Generating Sanitized Data, Sanitizing Anomaly Detection Models, And/Or Generating Sanitized Anomaly Detection Models
  • Performing Vocabulary-Based Visual Search Using Multi-Resolution Feature Descriptors
  • Ensemble Learning System And Method
  • Method And System For Network-Based Detecting Of Malware From Behavioral Clustering
  • Performing Vocabulary-Based Visual Search Using Multi-Resolution Feature Descriptors
  • Multivariate Responses Using Classification And Regression Trees Systems And Methods
  • Method And System To Safely Guide Interventions In Procedures The Substrate Whereof Is Neuronal Plasticity
  • Generating Training Documents
  • Method And System For Meshing Human And Computer Competencies For Object Categorization
  • Historical Analysis To Identify Malicious Activity
  • Method And System For Detecting Dga-Based Malware
  • System And Method For Pain Monitoring Using A Multidimensional Analysis Of Physiological Signals
  • Methods And Apparatus For User Interface Optimization
  • Method And System For Detecting Malware
  • Measuring, Categorizing, And/Or Mitigating Malware Distribution Paths
  • Method And System For Detecting Malicious And/Or Botnet-Related Domain Names
  • Method And System For Determining Whether Domain Names Are Legitimate Or Malicious
  • System And Method For Image Sequence Processing
  • Data Mining To Identify Malicious Activity
  • An Improved Stacking Schema For Classification Tasks
  • Systems, Methods, And Media Protecting A Digital Data Processing Device From Attack
  • Method For Screening And Treating Patients At Risk Of Medical Disorders
  • Binary Tree For Complex Supervised Learning
  • Methods And Systems For Detecting Compromised Computers
  • Method And System For Detecting Malicious Domain Names At An Upper Dns Hierarchy
  • System And Method Of Designing Models In A Feedback Loop
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00058655

    DOI

    http://dx.doi.org/10.1007/bf00058655

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1002929950


    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": "University of California, Berkeley", 
              "id": "https://www.grid.ac/institutes/grid.47840.3f", 
              "name": [
                "Statistics Department, University of California, 94720, Berkeley, CA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Breiman", 
            "givenName": "Leo", 
            "id": "sg:person.01275565034.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01275565034.02"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/b978-0-444-88650-7.50030-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037480404"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.87.23.9193", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044205330"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1214/aos/1176347963", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045549108"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/01621459.1985.10478157", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058303134"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/1403680", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069473952"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4899-4537-2_15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1089746414", 
              "https://doi.org/10.1007/978-1-4899-4537-2_15"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/0471725153", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109700913"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/0471725153", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109700913"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1109705929", 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4899-4541-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109705929", 
              "https://doi.org/10.1007/978-1-4899-4541-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4899-4541-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1109705929", 
              "https://doi.org/10.1007/978-1-4899-4541-9"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1996-08", 
        "datePublishedReg": "1996-08-01", 
        "description": "Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/bf00058655", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1125588", 
            "issn": [
              "0885-6125", 
              "1573-0565"
            ], 
            "name": "Machine Learning", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "24"
          }
        ], 
        "name": "Bagging predictors", 
        "pagination": "123-140", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/bf00058655"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "0df45a73282c1c98a576bae95d50df4ebcdda05594e10e00541e30392b93b6a3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1002929950"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/bf00058655", 
          "https://app.dimensions.ai/details/publication/pub.1002929950"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-15T08:50", 
        "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/v90/0000000374_0000000374/records_119730_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/BF00058655"
      }
    ]
     

    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/bf00058655'

    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/bf00058655'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00058655'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00058655'


     

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

    89 TRIPLES      21 PREDICATES      36 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/bf00058655 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N780c1355f1c8426e942d5d18bdc4c275
    4 schema:citation sg:pub.10.1007/978-1-4899-4537-2_15
    5 sg:pub.10.1007/978-1-4899-4541-9
    6 https://app.dimensions.ai/details/publication/pub.1109705929
    7 https://doi.org/10.1002/0471725153
    8 https://doi.org/10.1016/b978-0-444-88650-7.50030-5
    9 https://doi.org/10.1073/pnas.87.23.9193
    10 https://doi.org/10.1080/01621459.1985.10478157
    11 https://doi.org/10.1214/aos/1176347963
    12 https://doi.org/10.2307/1403680
    13 schema:datePublished 1996-08
    14 schema:datePublishedReg 1996-08-01
    15 schema:description Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.
    16 schema:genre research_article
    17 schema:inLanguage en
    18 schema:isAccessibleForFree true
    19 schema:isPartOf N3e39ddea81184817888d21f2d8b99938
    20 N56ff9bbcaf1240e8a76789061991bce8
    21 sg:journal.1125588
    22 schema:name Bagging predictors
    23 schema:pagination 123-140
    24 schema:productId N06ddee796f4d4ae29a65bac659daab2a
    25 N12bcf0fb35824df493e96ca9dab5ea36
    26 N51e0c36253464aa0b3b34f3be5b9ad13
    27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002929950
    28 https://doi.org/10.1007/bf00058655
    29 schema:sdDatePublished 2019-04-15T08:50
    30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    31 schema:sdPublisher Ncd091070df6a4f00ab3dddaa35db82f7
    32 schema:url http://link.springer.com/10.1007/BF00058655
    33 sgo:license sg:explorer/license/
    34 sgo:sdDataset articles
    35 rdf:type schema:ScholarlyArticle
    36 N06ddee796f4d4ae29a65bac659daab2a schema:name readcube_id
    37 schema:value 0df45a73282c1c98a576bae95d50df4ebcdda05594e10e00541e30392b93b6a3
    38 rdf:type schema:PropertyValue
    39 N12bcf0fb35824df493e96ca9dab5ea36 schema:name doi
    40 schema:value 10.1007/bf00058655
    41 rdf:type schema:PropertyValue
    42 N3e39ddea81184817888d21f2d8b99938 schema:volumeNumber 24
    43 rdf:type schema:PublicationVolume
    44 N51e0c36253464aa0b3b34f3be5b9ad13 schema:name dimensions_id
    45 schema:value pub.1002929950
    46 rdf:type schema:PropertyValue
    47 N56ff9bbcaf1240e8a76789061991bce8 schema:issueNumber 2
    48 rdf:type schema:PublicationIssue
    49 N780c1355f1c8426e942d5d18bdc4c275 rdf:first sg:person.01275565034.02
    50 rdf:rest rdf:nil
    51 Ncd091070df6a4f00ab3dddaa35db82f7 schema:name Springer Nature - SN SciGraph project
    52 rdf:type schema:Organization
    53 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    54 schema:name Information and Computing Sciences
    55 rdf:type schema:DefinedTerm
    56 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    57 schema:name Artificial Intelligence and Image Processing
    58 rdf:type schema:DefinedTerm
    59 sg:journal.1125588 schema:issn 0885-6125
    60 1573-0565
    61 schema:name Machine Learning
    62 rdf:type schema:Periodical
    63 sg:person.01275565034.02 schema:affiliation https://www.grid.ac/institutes/grid.47840.3f
    64 schema:familyName Breiman
    65 schema:givenName Leo
    66 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01275565034.02
    67 rdf:type schema:Person
    68 sg:pub.10.1007/978-1-4899-4537-2_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1089746414
    69 https://doi.org/10.1007/978-1-4899-4537-2_15
    70 rdf:type schema:CreativeWork
    71 sg:pub.10.1007/978-1-4899-4541-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109705929
    72 https://doi.org/10.1007/978-1-4899-4541-9
    73 rdf:type schema:CreativeWork
    74 https://app.dimensions.ai/details/publication/pub.1109705929 schema:CreativeWork
    75 https://doi.org/10.1002/0471725153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109700913
    76 rdf:type schema:CreativeWork
    77 https://doi.org/10.1016/b978-0-444-88650-7.50030-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037480404
    78 rdf:type schema:CreativeWork
    79 https://doi.org/10.1073/pnas.87.23.9193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044205330
    80 rdf:type schema:CreativeWork
    81 https://doi.org/10.1080/01621459.1985.10478157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303134
    82 rdf:type schema:CreativeWork
    83 https://doi.org/10.1214/aos/1176347963 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045549108
    84 rdf:type schema:CreativeWork
    85 https://doi.org/10.2307/1403680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069473952
    86 rdf:type schema:CreativeWork
    87 https://www.grid.ac/institutes/grid.47840.3f schema:alternateName University of California, Berkeley
    88 schema:name Statistics Department, University of California, 94720, Berkeley, CA
    89 rdf:type schema:Organization
     




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


    ...