CN2 Algorithm

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See: Rule Induction Algorithm, AQ Algorithm, ID3 Algorithm.



    • QUOTE: The CN2 induction algorithm is an learning algorithm for rule induction. 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.

      The algorithm must be given a set of examples, TrainingSet, which have already been classified in order to generate a list of classification rules. A set of conditions, SimpleConditionSet, which can be applied, alone or in combination, to any set of examples is predefined to be used for the classification.


  • (Clark & Nibblet, 1989) ⇒ Peter Clark, and Tim Niblett. (1989). “The CN2 Induction Algorithm.” In: Machine Learning, 3(4). doi:10.1023/A:1022641700528
    • ABSTRACT: Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the CN2, ID3, and AQ algorithms are compared on three medical classification tasks.