Rule-based machine learning
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In computer science, rule-based machine learning (RBML) is a general term intended to encompass any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge[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. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. 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.
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 it’s condition is satisfied. Therefore rule-based machine learning methods typically identify a set of rules that collectively comprise the prediction model, or the knowledge base.
See also
- Learning classifier system
- Association rule learning
- Artificial immune system
- Expert system
- Decision rule
- Rule induction
- Logic programming
- Rule-based machine translation
- Genetic algorithm
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. doi:10.1105/tpc.111.088153. ISSN 1532-298X.
- ^ M., Weiss, S.; N., Indurkhya, (1995-01-01). "Rule-based Machine Learning Methods for Functional Prediction". Journal of Artificial Intelligence Research. 3. doi:10.1613/jair.199.
{{cite journal}}: CS1 maint: extra punctuation (link) CS1 maint: multiple names: authors list (link) - ^ "GECCO 2016 | Tutorials". GECCO 2016. Retrieved 2016-10-14.