Complexity Control in Rule Based Models for Classification in Machine Learning Context View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Han Liu , Alexander Gegov , Mihaela Cocea

ABSTRACT

A rule based model is a special type of computational models, which can be built by using expert knowledge or learning from real data. In this context, rule based modelling approaches can be divided into two categories: expert based approaches and data based approaches. Due to the vast and rapid increase in data, the latter approach has become increasingly popular for building rule based models. In machine learning context, rule based models can be evaluated in three main dimensions, namely accuracy, efficiency and interpretability. All these dimensions are usually affected by the key characteristic of a rule based model which is typically referred to as model complexity. This paper focuses on theoretical and empirical analysis of complexity of rule based models, especially for classification tasks. In particular, the significance of model complexity is argued and a list of impact factors against the complexity are identified. This paper also proposes several techniques for effective control of model complexity, and experimental studies are reported for presentation and discussion of results in order to analyze critically and comparatively the extent to which the proposed techniques are effective in control of model complexity. More... »

PAGES

125-143

Book

TITLE

Advances in Computational Intelligence Systems

ISBN

978-3-319-46561-6
978-3-319-46562-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46562-3_9

DOI

http://dx.doi.org/10.1007/978-3-319-46562-3_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016714323


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 Portsmouth", 
          "id": "https://www.grid.ac/institutes/grid.4701.2", 
          "name": [
            "School of Computing, University of Portsmouth"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Han", 
        "id": "sg:person.010224527561.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010224527561.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Portsmouth", 
          "id": "https://www.grid.ac/institutes/grid.4701.2", 
          "name": [
            "School of Computing, University of Portsmouth"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gegov", 
        "givenName": "Alexander", 
        "id": "sg:person.012735463105.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012735463105.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Portsmouth", 
          "id": "https://www.grid.ac/institutes/grid.4701.2", 
          "name": [
            "School of Computing, University of Portsmouth"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cocea", 
        "givenName": "Mihaela", 
        "id": "sg:person.07627677267.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07627677267.93"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1002727234", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-23696-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002727234", 
          "https://doi.org/10.1007/978-3-319-23696-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-23696-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002727234", 
          "https://doi.org/10.1007/978-3-319-23696-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-08254-7_10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003135047", 
          "https://doi.org/10.1007/978-3-319-08254-7_10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-11071-4_18", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006281159", 
          "https://doi.org/10.1007/978-3-319-11071-4_18"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1006524209794", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022058703", 
          "https://doi.org/10.1023/a:1006524209794"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0020-7373(87)80003-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026476163"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-27267-2_7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041231591", 
          "https://doi.org/10.1007/978-3-319-27267-2_7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00993504", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041330751", 
          "https://doi.org/10.1007/bf00993504"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-30319-2_9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047584804", 
          "https://doi.org/10.1007/978-3-319-30319-2_9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/fuzz-ieee.2015.7337807", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095103174"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1613/jair.816", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105579526"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017", 
    "datePublishedReg": "2017-01-01", 
    "description": "A rule based model is a special type of computational models, which can be built by using expert knowledge or learning from real data. In this context, rule based modelling approaches can be divided into two categories: expert based approaches and data based approaches. Due to the vast and rapid increase in data, the latter approach has become increasingly popular for building rule based models. In machine learning context, rule based models can be evaluated in three main dimensions, namely accuracy, efficiency and interpretability. All these dimensions are usually affected by the key characteristic of a rule based model which is typically referred to as model complexity. This paper focuses on theoretical and empirical analysis of complexity of rule based models, especially for classification tasks. In particular, the significance of model complexity is argued and a list of impact factors against the complexity are identified. This paper also proposes several techniques for effective control of model complexity, and experimental studies are reported for presentation and discussion of results in order to analyze critically and comparatively the extent to which the proposed techniques are effective in control of model complexity.", 
    "editor": [
      {
        "familyName": "Angelov", 
        "givenName": "Plamen", 
        "type": "Person"
      }, 
      {
        "familyName": "Gegov", 
        "givenName": "Alexander", 
        "type": "Person"
      }, 
      {
        "familyName": "Jayne", 
        "givenName": "Chrisina", 
        "type": "Person"
      }, 
      {
        "familyName": "Shen", 
        "givenName": "Qiang", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-46562-3_9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-46561-6", 
        "978-3-319-46562-3"
      ], 
      "name": "Advances in Computational Intelligence Systems", 
      "type": "Book"
    }, 
    "name": "Complexity Control in Rule Based Models for Classification in Machine Learning Context", 
    "pagination": "125-143", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-46562-3_9"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4fa308b7af9d90a784cd523338b16f968701126c132df7a762d432a63dce63b5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1016714323"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-46562-3_9", 
      "https://app.dimensions.ai/details/publication/pub.1016714323"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T12:11", 
    "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_8660_00000584.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-46562-3_9"
  }
]
 

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.10.1007/978-3-319-46562-3_9'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-46562-3_9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-46562-3_9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-46562-3_9'


 

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

133 TRIPLES      23 PREDICATES      38 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-46562-3_9 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N92ff246b29cc4dfba8a50c51f9c9fbff
4 schema:citation sg:pub.10.1007/978-3-319-08254-7_10
5 sg:pub.10.1007/978-3-319-11071-4_18
6 sg:pub.10.1007/978-3-319-23696-4
7 sg:pub.10.1007/978-3-319-27267-2_7
8 sg:pub.10.1007/978-3-319-30319-2_9
9 sg:pub.10.1007/bf00993504
10 sg:pub.10.1023/a:1006524209794
11 https://app.dimensions.ai/details/publication/pub.1002727234
12 https://doi.org/10.1016/s0020-7373(87)80003-2
13 https://doi.org/10.1109/fuzz-ieee.2015.7337807
14 https://doi.org/10.1613/jair.816
15 schema:datePublished 2017
16 schema:datePublishedReg 2017-01-01
17 schema:description A rule based model is a special type of computational models, which can be built by using expert knowledge or learning from real data. In this context, rule based modelling approaches can be divided into two categories: expert based approaches and data based approaches. Due to the vast and rapid increase in data, the latter approach has become increasingly popular for building rule based models. In machine learning context, rule based models can be evaluated in three main dimensions, namely accuracy, efficiency and interpretability. All these dimensions are usually affected by the key characteristic of a rule based model which is typically referred to as model complexity. This paper focuses on theoretical and empirical analysis of complexity of rule based models, especially for classification tasks. In particular, the significance of model complexity is argued and a list of impact factors against the complexity are identified. This paper also proposes several techniques for effective control of model complexity, and experimental studies are reported for presentation and discussion of results in order to analyze critically and comparatively the extent to which the proposed techniques are effective in control of model complexity.
18 schema:editor N4c28900c02624aaa8fad210b42408644
19 schema:genre chapter
20 schema:inLanguage en
21 schema:isAccessibleForFree false
22 schema:isPartOf Nd587354aa77041cbb80030b4694f858b
23 schema:name Complexity Control in Rule Based Models for Classification in Machine Learning Context
24 schema:pagination 125-143
25 schema:productId N17e0973944a949e7966fe9a40bd8df21
26 N45c1824b6c81427ca86a764fe94d665e
27 Nf89d517f7fc5438583fee349b832bc33
28 schema:publisher Nc9a0cf8f969641b188e37de293e3e342
29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016714323
30 https://doi.org/10.1007/978-3-319-46562-3_9
31 schema:sdDatePublished 2019-04-15T12:11
32 schema:sdLicense https://scigraph.springernature.com/explorer/license/
33 schema:sdPublisher N93d3b5f9f1a5483f8c323302a0e581a0
34 schema:url http://link.springer.com/10.1007/978-3-319-46562-3_9
35 sgo:license sg:explorer/license/
36 sgo:sdDataset chapters
37 rdf:type schema:Chapter
38 N17e0973944a949e7966fe9a40bd8df21 schema:name readcube_id
39 schema:value 4fa308b7af9d90a784cd523338b16f968701126c132df7a762d432a63dce63b5
40 rdf:type schema:PropertyValue
41 N3259b2ed294d42e28bcc7c612a52fdc6 rdf:first N75c6798be18240a7825e9a92b12a5a44
42 rdf:rest rdf:nil
43 N45c1824b6c81427ca86a764fe94d665e schema:name doi
44 schema:value 10.1007/978-3-319-46562-3_9
45 rdf:type schema:PropertyValue
46 N4c28900c02624aaa8fad210b42408644 rdf:first Neaf3eff64fb8453fa2da9802e0ae1ab0
47 rdf:rest N6982fff22f1746aca18dc1b21730e172
48 N6982fff22f1746aca18dc1b21730e172 rdf:first N93719e6b52d940bb9dfafe2c0cc30487
49 rdf:rest Nbdf325b04dbc421f94d1cbe1573a78ee
50 N75c6798be18240a7825e9a92b12a5a44 schema:familyName Shen
51 schema:givenName Qiang
52 rdf:type schema:Person
53 N7dcd98bd79fa42cc9d1aeb929df037ac rdf:first sg:person.07627677267.93
54 rdf:rest rdf:nil
55 N92ff246b29cc4dfba8a50c51f9c9fbff rdf:first sg:person.010224527561.51
56 rdf:rest Nb35e247ffc8b4e2983b5fe61dcf77ce0
57 N93719e6b52d940bb9dfafe2c0cc30487 schema:familyName Gegov
58 schema:givenName Alexander
59 rdf:type schema:Person
60 N93d3b5f9f1a5483f8c323302a0e581a0 schema:name Springer Nature - SN SciGraph project
61 rdf:type schema:Organization
62 Nb35e247ffc8b4e2983b5fe61dcf77ce0 rdf:first sg:person.012735463105.50
63 rdf:rest N7dcd98bd79fa42cc9d1aeb929df037ac
64 Nb97a9657918f4616aafe1ef8957a571a schema:familyName Jayne
65 schema:givenName Chrisina
66 rdf:type schema:Person
67 Nbdf325b04dbc421f94d1cbe1573a78ee rdf:first Nb97a9657918f4616aafe1ef8957a571a
68 rdf:rest N3259b2ed294d42e28bcc7c612a52fdc6
69 Nc9a0cf8f969641b188e37de293e3e342 schema:location Cham
70 schema:name Springer International Publishing
71 rdf:type schema:Organisation
72 Nd587354aa77041cbb80030b4694f858b schema:isbn 978-3-319-46561-6
73 978-3-319-46562-3
74 schema:name Advances in Computational Intelligence Systems
75 rdf:type schema:Book
76 Neaf3eff64fb8453fa2da9802e0ae1ab0 schema:familyName Angelov
77 schema:givenName Plamen
78 rdf:type schema:Person
79 Nf89d517f7fc5438583fee349b832bc33 schema:name dimensions_id
80 schema:value pub.1016714323
81 rdf:type schema:PropertyValue
82 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
83 schema:name Information and Computing Sciences
84 rdf:type schema:DefinedTerm
85 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
86 schema:name Artificial Intelligence and Image Processing
87 rdf:type schema:DefinedTerm
88 sg:person.010224527561.51 schema:affiliation https://www.grid.ac/institutes/grid.4701.2
89 schema:familyName Liu
90 schema:givenName Han
91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010224527561.51
92 rdf:type schema:Person
93 sg:person.012735463105.50 schema:affiliation https://www.grid.ac/institutes/grid.4701.2
94 schema:familyName Gegov
95 schema:givenName Alexander
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012735463105.50
97 rdf:type schema:Person
98 sg:person.07627677267.93 schema:affiliation https://www.grid.ac/institutes/grid.4701.2
99 schema:familyName Cocea
100 schema:givenName Mihaela
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07627677267.93
102 rdf:type schema:Person
103 sg:pub.10.1007/978-3-319-08254-7_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003135047
104 https://doi.org/10.1007/978-3-319-08254-7_10
105 rdf:type schema:CreativeWork
106 sg:pub.10.1007/978-3-319-11071-4_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006281159
107 https://doi.org/10.1007/978-3-319-11071-4_18
108 rdf:type schema:CreativeWork
109 sg:pub.10.1007/978-3-319-23696-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002727234
110 https://doi.org/10.1007/978-3-319-23696-4
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/978-3-319-27267-2_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041231591
113 https://doi.org/10.1007/978-3-319-27267-2_7
114 rdf:type schema:CreativeWork
115 sg:pub.10.1007/978-3-319-30319-2_9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047584804
116 https://doi.org/10.1007/978-3-319-30319-2_9
117 rdf:type schema:CreativeWork
118 sg:pub.10.1007/bf00993504 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041330751
119 https://doi.org/10.1007/bf00993504
120 rdf:type schema:CreativeWork
121 sg:pub.10.1023/a:1006524209794 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022058703
122 https://doi.org/10.1023/a:1006524209794
123 rdf:type schema:CreativeWork
124 https://app.dimensions.ai/details/publication/pub.1002727234 schema:CreativeWork
125 https://doi.org/10.1016/s0020-7373(87)80003-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026476163
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1109/fuzz-ieee.2015.7337807 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095103174
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1613/jair.816 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105579526
130 rdf:type schema:CreativeWork
131 https://www.grid.ac/institutes/grid.4701.2 schema:alternateName University of Portsmouth
132 schema:name School of Computing, University of Portsmouth
133 rdf:type schema:Organization
 




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


...