Rule-based machine learning: Difference between revisions
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Adding local short description: "AI that learns decision rules from data", overriding Wikidata description "machine learning methods that try to develop rules that are to be applied in particular contexts" |
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{{Short description|AI that learns decision rules from data}} |
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{{Machine learning|Paradigms}} |
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'''Rule-based machine learning''' (RBML) is a term in [[computer science]] intended to encompass any [[machine learning]] method that identifies, learns, or evolves 'rules' to store, manipulate or apply.<ref> |
'''Rule-based machine learning''' (RBML) is a term in [[computer science]] intended to encompass any [[machine learning]] method that identifies, learns, or evolves 'rules' to store, manipulate or apply.<ref> |
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{{Cite journal| |
{{Cite journal|last1=Bassel|first1=George W.|last2=Glaab|first2=Enrico|last3=Marquez|first3=Julietta |last4=Holdsworth |first4=Michael J. |last5=Bacardit |first5=Jaume |date=2011-09-01 |title=Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets |journal=The Plant Cell |language=en |volume=23 |issue=9 |pages=3101–3116 |doi=10.1105/tpc.111.088153 |pmid=21896882 |issn=1532-298X |pmc=3203449 |
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| ⚫ | </ref><ref>{{Cite journal| |
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| ⚫ | </ref><ref>{{Cite journal|last1=M.|first1=Weiss, S. |last2=N. |first2=Indurkhya |date=1995-01-01 |title=Rule-based Machine Learning Methods for Functional Prediction |url=http://jair.org/papers/paper199.html|journal=Journal of Artificial Intelligence Research |volume=3 |issue=1995 |pages=383–403 |doi=10.1613/jair.199 |arxiv=cs/9512107|bibcode=1995cs.......12107W |s2cid=1588466 }} |
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{{Cite web|url=http://gecco-2016.sigevo.org/index.html/Tutorials#id_Introducing%20rule-based%20machine%20learning:%20capturing%20complexity |title=GECCO 2016 {{!}} Tutorials |website=GECCO 2016 |access-date=2016-10-14 |
{{Cite web|url=http://gecco-2016.sigevo.org/index.html/Tutorials#id_Introducing%20rule-based%20machine%20learning:%20capturing%20complexity |title=GECCO 2016 {{!}} Tutorials |website=GECCO 2016 |access-date=2016-10-14 |
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</ref> |
</ref> The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. |
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Rule-based machine learning approaches include [[learning classifier system]]s,<ref> |
Rule-based machine learning approaches include [[learning classifier system]]s,<ref> |
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{{Cite journal | |
{{Cite journal |last1=Urbanowicz |first1=Ryan J. |last2=Moore |first2=Jason H. |date=2009-09-22 |title=Learning Classifier Systems: A Complete Introduction, Review, and Roadmap |journal=Journal of Artificial Evolution and Applications |language=en |volume=2009 |pages=1–25 |doi=10.1155/2009/736398 |issn=1687-6229 |doi-access=free }} |
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| ⚫ | </ref> [[association rule learning]],<ref>Zhang, C. and Zhang, S., 2002. ''[https://books.google.com/books?id=VqSoCAAAQBAJ Association rule mining: models and algorithms]''. Springer-Verlag.</ref> [[artificial immune system]]s,<ref>De Castro, Leandro Nunes, and Jonathan Timmis. ''[https://books.google.com/books?id=aMFP7p8DtaQC&q=%22rule-based%22 Artificial immune systems: a new computational intelligence approach]''. Springer Science & Business Media, 2002.</ref> and any other method that relies on a set of rules, each covering contextual knowledge. |
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| ⚫ | </ref> [[association rule learning]],<ref>Zhang, C. and Zhang, S., 2002. ''Association rule mining: models and algorithms''. Springer-Verlag.</ref> [[artificial immune system]]s,<ref>De Castro, Leandro Nunes, and Jonathan Timmis. ''Artificial immune systems: a new computational intelligence approach''. Springer Science & Business Media, 2002.</ref> and any other method that relies on a set of rules, each covering contextual knowledge. |
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While rule-based machine learning is conceptually a type of rule-based system, it is distinct from traditional [[rule-based system]]s, which are often hand-crafted, and other rule-based decision makers. |
While rule-based machine learning is conceptually a type of rule-based system, it is distinct from traditional [[rule-based system]]s, which are often hand-crafted, and other rule-based decision makers. This is because rule-based machine learning applies some form of learning algorithm to automatically identify useful rules, rather than a human needing to apply prior [[domain knowledge]] to manually construct rules and curate a rule set. |
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== Rules == |
== Rules == |
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Rules typically take the form of an {IF:THEN} expression, (e.g. |
Rules typically take the form of an '''''<nowiki />'{IF:THEN} expression'''''', (e.g. {''IF 'condition' THEN 'result'},'' or as a more specific example, ''{IF 'red' AND 'octagon' THEN 'stop-sign}''). An individual rule is not in itself a model, since the rule is only applicable when its condition is satisfied. Therefore rule-based machine learning methods typically comprise a set of rules, or [[knowledge base]], that collectively make up the prediction model. |
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== See also == |
== See also == |
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* [[Learning classifier system]] |
* [[Learning classifier system]] |
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* [[Association rule learning]] |
* [[Association rule learning]] |
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* [[Associative classifier]] |
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* [[Artificial immune system]] |
* [[Artificial immune system]] |
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* [[Expert system]] |
* [[Expert system]] |
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* [[Decision rule]] |
* [[Decision rule]] |
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* [[Rule induction]] |
* [[Rule induction]] |
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* [[ |
* [[Inductive logic programming]] |
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* [[Rule-based machine translation]] |
* [[Rule-based machine translation]] |
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* [[Genetic algorithm]] |
* [[Genetic algorithm]] |
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* [[RuleML]] |
* [[RuleML]] |
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* [[Production system (computer science)|Production rule system]] |
* [[Production system (computer science)|Production rule system]] |
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* [[ |
* [[Business rule engine]] |
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* [[Business rule management system]] |
* [[Business rule management system]] |
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{{colend}} |
{{colend}} |
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Latest revision as of 09:40, 6 April 2024
| Part of a series on |
| Machine learning and data mining |
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Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply.[1][2][3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.
Rule-based machine learning approaches include learning classifier systems,[4] association rule learning,[5] artificial immune systems,[6] and any other method that relies on a set of rules, each covering contextual knowledge.
While rule-based machine learning is conceptually a type of rule-based system, it is distinct from traditional rule-based systems, which are often hand-crafted, and other rule-based decision makers. This is because rule-based machine learning applies some form of learning algorithm to automatically identify useful rules, rather than a human needing to apply prior domain knowledge to manually construct rules and curate a rule set.
Rules
[edit]Rules typically take the form of an '{IF:THEN} expression', (e.g. {IF 'condition' THEN 'result'}, or as a more specific example, {IF 'red' AND 'octagon' THEN 'stop-sign}). An individual rule is not in itself a model, since the rule is only applicable when its condition is satisfied. Therefore rule-based machine learning methods typically comprise a set of rules, or knowledge base, that collectively make up the prediction model.
See also
[edit]- Learning classifier system
- Association rule learning
- Associative classifier
- Artificial immune system
- Expert system
- Decision rule
- Rule induction
- Inductive logic programming
- Rule-based machine translation
- Genetic algorithm
- Rule-based system
- Rule-based programming
- RuleML
- Production rule system
- Business rule engine
- Business rule management system
References
[edit]- ^ Bassel, George W.; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J.; Bacardit, Jaume (2011-09-01). "Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets". The Plant Cell. 23 (9): 3101–3116. doi:10.1105/tpc.111.088153. ISSN 1532-298X. PMC 3203449. PMID 21896882.
- ^ M., Weiss, S.; N., Indurkhya (1995-01-01). "Rule-based Machine Learning Methods for Functional Prediction". Journal of Artificial Intelligence Research. 3 (1995): 383–403. arXiv:cs/9512107. Bibcode:1995cs.......12107W. doi:10.1613/jair.199. S2CID 1588466.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ "GECCO 2016 | Tutorials". GECCO 2016. Retrieved 2016-10-14.
- ^ Urbanowicz, Ryan J.; Moore, Jason H. (2009-09-22). "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap". Journal of Artificial Evolution and Applications. 2009: 1–25. doi:10.1155/2009/736398. ISSN 1687-6229.
- ^ Zhang, C. and Zhang, S., 2002. Association rule mining: models and algorithms. Springer-Verlag.
- ^ De Castro, Leandro Nunes, and Jonathan Timmis. Artificial immune systems: a new computational intelligence approach. Springer Science & Business Media, 2002.