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The CN2 induction algorithm is a learning algorithm for rule induction. [1] It is designed to work even when the training data is imperfect. It is based on ideas from the AQ algorithm and the ID3 algorithm. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.
A step-wise schematic illustrating a generic Michigan-style learning classifier system learning cycle performing supervised learning. Keeping in mind that LCS is a paradigm for genetic-based machine learning rather than a specific method, the following outlines key elements of a generic, modern (i.e. post-XCS) LCS algorithm.
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 ...
A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. [1] For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game.
Data mining in general and rule induction in detail are trying to create algorithms without human programming but with analyzing existing data structures. [ 1 ] : 415- In the easiest case, a rule is expressed with “if-then statements” and was created with the ID3 algorithm for decision tree learning.
Rule-based modeling is a modeling approach that uses a set of rules that indirectly specifies a mathematical model. The rule-set can either be translated into a model such as Markov chains or differential equations, or be treated using tools that directly work on the rule-set in place of a translated model, as the latter is typically much bigger.
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. [ 1 ]
Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network (SNN) paradigm, [1] developed by Marco Muselli, Senior Researcher at the Italian National Research Council CNR-IEIIT in Genoa.