A method for using machine learning to solve problems having either a "positive"
result (the event occurred) or a "negative" result (the event did not occur), in
which the probability of a positive result is very low and the consequences of
the positive result are significant. Training data is obtained and a subset of
that data is distilled for application to a machine learning system. The training
data includes some records corresponding to the positive result, some nearest neighbors
from the records corresponding to the negative result, and some other records corresponding
to the negative result. The machine learning system uses a co-evolution approach
to obtain a rule set for predicting results after a number of cycles. The machine
system uses a fitness function derived for use with the type of problem, such as
a fitness function based on the sensitivity and positive predictive value of the
rules. The rules are validated using the entire set of training data.
