ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2005

AUTHORS

Kyosuke Nishida , Koichiro Yamauchi , Takashi Omori

ABSTRACT

Most machine learning algorithms assume stationary environments, require a large number of training examples in advance, and begin the learning from scratch. In contrast, humans learn in changing environments with sequential training examples and leverage prior knowledge in new situations. To deal with real-world problems in changing environments, the ability to make human-like quick responses must be developed in machines.Many researchers have presented learning systems that assume the presence of hidden context and concept drift. In particular, several systems have been proposed that use ensembles of classifiers on sequential chunks of training examples. These systems can respond to gradual changes in large-scale data streams but have problems responding to sudden changes and leveraging prior knowledge of recurring contexts. Moreover, these are not pure online learning systems.We propose an online learning system that uses an ensemble of classifiers suited to recent training examples. We use experiments to show that this system can leverage prior knowledge of recurring contexts and is robust against various noise levels and types of drift. More... »

PAGES

176-185

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11494683_18

DOI

http://dx.doi.org/10.1007/11494683_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016442646


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.39158.36", 
          "name": [
            "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nishida", 
        "givenName": "Kyosuke", 
        "id": "sg:person.011031550477.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011031550477.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.39158.36", 
          "name": [
            "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yamauchi", 
        "givenName": "Koichiro", 
        "id": "sg:person.07364502315.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07364502315.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.39158.36", 
          "name": [
            "Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Omori", 
        "givenName": "Takashi", 
        "id": "sg:person.01263557346.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01263557346.07"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2005", 
    "datePublishedReg": "2005-01-01", 
    "description": "Most machine learning algorithms assume stationary environments, require a large number of training examples in advance, and begin the learning from scratch. In contrast, humans learn in changing environments with sequential training examples and leverage prior knowledge in new situations. To deal with real-world problems in changing environments, the ability to make human-like quick responses must be developed in machines.Many researchers have presented learning systems that assume the presence of hidden context and concept drift. In particular, several systems have been proposed that use ensembles of classifiers on sequential chunks of training examples. These systems can respond to gradual changes in large-scale data streams but have problems responding to sudden changes and leveraging prior knowledge of recurring contexts. Moreover, these are not pure online learning systems.We propose an online learning system that uses an ensemble of classifiers suited to recent training examples. We use experiments to show that this system can leverage prior knowledge of recurring contexts and is robust against various noise levels and types of drift.", 
    "editor": [
      {
        "familyName": "Oza", 
        "givenName": "Nikunj C.", 
        "type": "Person"
      }, 
      {
        "familyName": "Polikar", 
        "givenName": "Robi", 
        "type": "Person"
      }, 
      {
        "familyName": "Kittler", 
        "givenName": "Josef", 
        "type": "Person"
      }, 
      {
        "familyName": "Roli", 
        "givenName": "Fabio", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/11494683_18", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-26306-7", 
        "978-3-540-31578-0"
      ], 
      "name": "Multiple Classifier Systems", 
      "type": "Book"
    }, 
    "keywords": [
      "training examples", 
      "online learning system", 
      "learning system", 
      "large-scale data streams", 
      "prior knowledge", 
      "concept-drifting environment", 
      "leverage prior knowledge", 
      "ensemble of classifiers", 
      "classifier ensemble system", 
      "real-world problems", 
      "concept drift", 
      "sequential chunks", 
      "data streams", 
      "types of drift", 
      "most machine", 
      "hidden context", 
      "stationary environment", 
      "classifier", 
      "machine", 
      "quick response", 
      "new situation", 
      "environment", 
      "large number", 
      "system", 
      "chunks", 
      "algorithm", 
      "learning", 
      "scratch", 
      "example", 
      "ensemble", 
      "knowledge", 
      "context", 
      "noise level", 
      "streams", 
      "researchers", 
      "sudden change", 
      "situation", 
      "advances", 
      "experiments", 
      "number", 
      "drift", 
      "ability", 
      "humans", 
      "types", 
      "gradual change", 
      "levels", 
      "changes", 
      "contrast", 
      "presence", 
      "response", 
      "problem"
    ], 
    "name": "ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments", 
    "pagination": "176-185", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1016442646"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/11494683_18"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/11494683_18", 
      "https://app.dimensions.ai/details/publication/pub.1016442646"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-20T07:44", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/chapter/chapter_236.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/11494683_18"
  }
]
 

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.10.1007/11494683_18'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/11494683_18'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/11494683_18'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/11494683_18'


 

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

140 TRIPLES      23 PREDICATES      77 URIs      70 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/11494683_18 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nd53d10f2a1fa48c189c547c17eba4cfe
4 schema:datePublished 2005
5 schema:datePublishedReg 2005-01-01
6 schema:description Most machine learning algorithms assume stationary environments, require a large number of training examples in advance, and begin the learning from scratch. In contrast, humans learn in changing environments with sequential training examples and leverage prior knowledge in new situations. To deal with real-world problems in changing environments, the ability to make human-like quick responses must be developed in machines.Many researchers have presented learning systems that assume the presence of hidden context and concept drift. In particular, several systems have been proposed that use ensembles of classifiers on sequential chunks of training examples. These systems can respond to gradual changes in large-scale data streams but have problems responding to sudden changes and leveraging prior knowledge of recurring contexts. Moreover, these are not pure online learning systems.We propose an online learning system that uses an ensemble of classifiers suited to recent training examples. We use experiments to show that this system can leverage prior knowledge of recurring contexts and is robust against various noise levels and types of drift.
7 schema:editor N93dfb71cde5548289597509c0164aba9
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf Nd82eb6cefc52451ea2290213324b7459
12 schema:keywords ability
13 advances
14 algorithm
15 changes
16 chunks
17 classifier
18 classifier ensemble system
19 concept drift
20 concept-drifting environment
21 context
22 contrast
23 data streams
24 drift
25 ensemble
26 ensemble of classifiers
27 environment
28 example
29 experiments
30 gradual change
31 hidden context
32 humans
33 knowledge
34 large number
35 large-scale data streams
36 learning
37 learning system
38 levels
39 leverage prior knowledge
40 machine
41 most machine
42 new situation
43 noise level
44 number
45 online learning system
46 presence
47 prior knowledge
48 problem
49 quick response
50 real-world problems
51 researchers
52 response
53 scratch
54 sequential chunks
55 situation
56 stationary environment
57 streams
58 sudden change
59 system
60 training examples
61 types
62 types of drift
63 schema:name ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments
64 schema:pagination 176-185
65 schema:productId N544aa1844a304581b0b6f2d48c916670
66 Nf59ca4c0568842ab91a91b76b6b8a69a
67 schema:publisher Nd4b0a7176aba4100a653e9ce009105a9
68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016442646
69 https://doi.org/10.1007/11494683_18
70 schema:sdDatePublished 2022-05-20T07:44
71 schema:sdLicense https://scigraph.springernature.com/explorer/license/
72 schema:sdPublisher N006b9f96b0264817872c7a5b7fd9f5c5
73 schema:url https://doi.org/10.1007/11494683_18
74 sgo:license sg:explorer/license/
75 sgo:sdDataset chapters
76 rdf:type schema:Chapter
77 N006b9f96b0264817872c7a5b7fd9f5c5 schema:name Springer Nature - SN SciGraph project
78 rdf:type schema:Organization
79 N26b28665bc2240dd9812a9ae1a157151 rdf:first sg:person.07364502315.92
80 rdf:rest Ncea183fd19354041971620caa80e8951
81 N544aa1844a304581b0b6f2d48c916670 schema:name dimensions_id
82 schema:value pub.1016442646
83 rdf:type schema:PropertyValue
84 N5b9469b80d4a4296a48c341df6685d08 rdf:first Ncd1a96083ab44273929a0c35d1a257c0
85 rdf:rest Ncb8a0b15583a46f8a9d4b2e067b3130e
86 N80c4f8eb669c4eea880a7ce30761d7f9 schema:familyName Roli
87 schema:givenName Fabio
88 rdf:type schema:Person
89 N93dfb71cde5548289597509c0164aba9 rdf:first Nb20ca829c49d4a049c9507bedbec0dae
90 rdf:rest Nd8937e78dd2549de938bb8a32ef07d34
91 Na321a948a66f4ecc8777d5ca7687721c schema:familyName Polikar
92 schema:givenName Robi
93 rdf:type schema:Person
94 Nb20ca829c49d4a049c9507bedbec0dae schema:familyName Oza
95 schema:givenName Nikunj C.
96 rdf:type schema:Person
97 Ncb8a0b15583a46f8a9d4b2e067b3130e rdf:first N80c4f8eb669c4eea880a7ce30761d7f9
98 rdf:rest rdf:nil
99 Ncd1a96083ab44273929a0c35d1a257c0 schema:familyName Kittler
100 schema:givenName Josef
101 rdf:type schema:Person
102 Ncea183fd19354041971620caa80e8951 rdf:first sg:person.01263557346.07
103 rdf:rest rdf:nil
104 Nd4b0a7176aba4100a653e9ce009105a9 schema:name Springer Nature
105 rdf:type schema:Organisation
106 Nd53d10f2a1fa48c189c547c17eba4cfe rdf:first sg:person.011031550477.82
107 rdf:rest N26b28665bc2240dd9812a9ae1a157151
108 Nd82eb6cefc52451ea2290213324b7459 schema:isbn 978-3-540-26306-7
109 978-3-540-31578-0
110 schema:name Multiple Classifier Systems
111 rdf:type schema:Book
112 Nd8937e78dd2549de938bb8a32ef07d34 rdf:first Na321a948a66f4ecc8777d5ca7687721c
113 rdf:rest N5b9469b80d4a4296a48c341df6685d08
114 Nf59ca4c0568842ab91a91b76b6b8a69a schema:name doi
115 schema:value 10.1007/11494683_18
116 rdf:type schema:PropertyValue
117 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
118 schema:name Information and Computing Sciences
119 rdf:type schema:DefinedTerm
120 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
121 schema:name Artificial Intelligence and Image Processing
122 rdf:type schema:DefinedTerm
123 sg:person.011031550477.82 schema:affiliation grid-institutes:grid.39158.36
124 schema:familyName Nishida
125 schema:givenName Kyosuke
126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011031550477.82
127 rdf:type schema:Person
128 sg:person.01263557346.07 schema:affiliation grid-institutes:grid.39158.36
129 schema:familyName Omori
130 schema:givenName Takashi
131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01263557346.07
132 rdf:type schema:Person
133 sg:person.07364502315.92 schema:affiliation grid-institutes:grid.39158.36
134 schema:familyName Yamauchi
135 schema:givenName Koichiro
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07364502315.92
137 rdf:type schema:Person
138 grid-institutes:grid.39158.36 schema:alternateName Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan
139 schema:name Graduate School of Information Science and Technology, Hokkaido University, Kita 14 Nishi 9, Kita, 060-0814, Sapporo, Japan
140 rdf:type schema:Organization
 




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


...