Rule-Based Learning

References

• Rule-Based Learning Wikipedia Article

Notes

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 ither method that relies on a set or 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. 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 create a rule set.

Rules typically take the form of an {IF:THEN} expression. 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.