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. 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, association rule learning, artificial immune systems, 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
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
- 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
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. Bibcode:2011PlanC..23.3101B. doi:10.1105/tpc.111.088153. ISSN 1532-298X. PMC 3203449. PMID 21896882. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3203449 ↩
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) http://jair.org/papers/paper199.html ↩
"GECCO 2016 | Tutorials". GECCO 2016. Retrieved 2016-10-14. http://gecco-2016.sigevo.org/index.html/Tutorials#id_Introducing%20rule-based%20machine%20learning:%20capturing%20complexity ↩
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. https://doi.org/10.1155%2F2009%2F736398 ↩
Zhang, C. and Zhang, S., 2002. Association rule mining: models and algorithms. Springer-Verlag. https://books.google.com/books?id=VqSoCAAAQBAJ ↩
De Castro, Leandro Nunes, and Jonathan Timmis. Artificial immune systems: a new computational intelligence approach. Springer Science & Business Media, 2002. https://books.google.com/books?id=aMFP7p8DtaQC&q=%22rule-based%22 ↩