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.
GA rule discovery fitness function
classification;genetic algorithms;evolutionary algorithms