It works based on the concept of separate-and-conquer to directly induce rules from a given training set and build its knowledge repository.
Algorithms under RULES family are usually available in data mining tools, such as KEEL and WEKA, known for knowledge extraction and decision making.
RULES family algorithms are mainly used in data mining to create a model that predicts the actions of a given input features.
In this type of learning, the agent is usually provided with previous information to gain descriptive knowledge based on the given historical data.
Thus, it is a supervised learning paradigm that works as a data analysis tool, which uses the knowledge gained through training to reach a general conclusion and identify new objects using the produced classifier.
Although DT algorithms was well recognized in the past few decades, CA started to attract the attention due to its direct rule induction property, as emphasized by Kurgan et al. [1].
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