Rule-based machine learning explained

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

Rules typically take the form of an ' expression', (e.g., or as a more specific example, ). 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

Notes and References

1. 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 . en . 23 . 9 . 3101–3116 . 10.1105/tpc.111.088153 . 21896882 . 1532-298X . 3203449.
2. 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 . 10.1613/jair.199 . cs/9512107. 1995cs.......12107W . 1588466 .
3. Web site: GECCO 2016 Tutorials . GECCO 2016 . 2016-10-14.
4. Urbanowicz . Ryan J. . Moore . Jason H. . 2009-09-22 . Learning Classifier Systems: A Complete Introduction, Review, and Roadmap . Journal of Artificial Evolution and Applications . en . 2009 . 1–25 . 10.1155/2009/736398 . 1687-6229 . free .
5. Zhang, C. and Zhang, S., 2002. Association rule mining: models and algorithms. Springer-Verlag.
6. De Castro, Leandro Nunes, and Jonathan Timmis. Artificial immune systems: a new computational intelligence approach. Springer Science & Business Media, 2002.