CN2 algorithm

The CN2 induction algorithm is a learning algorithm for rule induction.Clark, P. and Niblett, T (1989) The CN2 induction algorithm. Machine Learning 3(4):261-283. 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.

Description of algorithm

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.

routine CN2(TrainingSet)

let the ClassificationRuleList be empty

repeat

let the BestConditionExpression be Find_BestConditionExpression(TrainingSet)

if the BestConditionExpression is not nil

then

let the TrainingSubset be the examples covered by the BestConditionExpression

remove from the TrainingSet the examples in the TrainingSubset

let the MostCommonClass be the most common class of examples in the TrainingSubset

append to the ClassificationRuleList the rule

'if ' the BestConditionExpression ' then the class is ' the MostCommonClass

until the TrainingSet is empty or the BestConditionExpression is nil

return the ClassificationRuleList

routine Find_BestConditionExpression(TrainingSet)

let the ConditionalExpressionSet be empty

let the BestConditionExpression be nil

repeat

let the TrialConditionalExpressionSet be the set of conditional expressions,

{x and y where x belongs to the ConditionalExpressionSet and y belongs to the SimpleConditionSet}.

remove all formulae in the TrialConditionalExpressionSet that are either in the ConditionalExpressionSet (i.e.,

the unspecialized ones) or null (e.g., big = y and big = n)

for every expression, F, in the TrialConditionalExpressionSet

F is statistically significant

and F is better than the BestConditionExpression

by user-defined criteria when tested on the TrainingSet

then

replace the current value of the BestConditionExpression by F

while the number of expressions in the TrialConditionalExpressionSet > user-defined maximum

remove the worst expression from the TrialConditionalExpressionSet

let the ConditionalExpressionSet be the TrialConditionalExpressionSet

until the ConditionalExpressionSet is empty

return the BestConditionExpression

References

External links

[https://doi.org/10.1023%2FA%3A1022641700528 CN2 Algorithm Description]

Category:Machine learning algorithms