Rule-based machine learning (RBML) is a term in

computer science Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...

intended to encompass any

machine learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...

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 Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.P ...

, 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 system In computer science, a rule-based system is a computer system in which domain-specific knowledge is represented in the form of rules and general-purpose reasoning is used to solve problems in the domain. Two different kinds of rule-based systems ...

s, which are often hand-crafted, and other rule-based decision makers. This is because rule-based machine learning applies some form of learning algorithm such as Rough sets theoryISBN 978-0-7923-1472-1. to identify and minimise the set of features and to automatically identify useful rules, rather than a human needing to apply prior

domain knowledge Domain knowledge is knowledge of a specific discipline or field in contrast to general (or domain-independent) knowledge. The term is often used in reference to a more general discipline—for example, in describing a software engineer who has ge ...

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 In computer science, a knowledge base (KB) is a set of sentences, each sentence given in a knowledge representation language, with interfaces to tell new sentences and to ask questions about what is known, where either of these interfaces migh ...

, that collectively make up the prediction model usually know as decision algorithm. Rules can also be interpreted in various ways depending on the domain knowledge, data types(discrete or continuous) and in combinations.

RIPPER

''Repeated incremental pruning to produce error reduction'' (RIPPER) is a propositional rule learner proposed by William W. Cohen as an optimized version of IREP.

References

{{reflist Machine learning algorithms

Rules

RIPPER

See also

References