[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.
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 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}).
Therefore rule-based machine learning methods typically comprise a set of rules, or knowledge base, that collectively make up the prediction model.