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. 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 system
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement lear ...
s,
association rule learning,
artificial immune system In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modele ...
s,
[De Castro, Leandro Nunes, and Jonathan Timmis. ]
Artificial immune systems: a new computational intelligence approach
'. Springer Science & Business Media, 2002. 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
Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement lear ...
*
Association rule learning
*
Associative classifier
An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., in which the authors defined a model made of rules "who ...
*
Artificial immune system In artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modele ...
*
Expert system
In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert.
Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if� ...
*
Decision rule
*
Rule induction
*
Inductive logic programming
*
Rule-based machine translation
*
Genetic algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to gene ...
*
Rule-based system
*
Rule-based programming
*
RuleML
*
Production rule system
*
Business rule engine
*
Business rule management system
References
{{reflist
Machine learning algorithms