# GA rule discovery fitness function

I'm attempting to develop a genetic algorithm capable of discovering classification rules for a given data set, a number of papers make use of the Confidence (precision) and Coverage of a rule to define its fitness.

However I'm not sure my understanding of the equations is correct.

For example confidence is:

conf = |P & D| / |P|

And is defined as follows; "In classification problems, confidence measure is defined as the ratio of the number of examples in P that are correctly classified as decision class of D and the number of examples in P."

Is this saying, the total number of occurrences of the attributes in a given rule P which occur in rules which have been classified as class D, by the number of attributes in P ?

Where an example of a rule containing two attributes would be as follows:

(martial_status = married & age > 30)

It seems a number of papers define it differently which has led to my confusion, if anyone is able to confirm my understanding or provide an some insight that'd be great.

Edit:

The research paper I've been following can be found here.

 P     : Refers to the set of samples in your dataset. (Selected elements)