abstract = "This work is motivated by financial forecasting using
Genetic Programming. This paper presents a method to
post-process decision trees. The processing procedure
is based on the analysis and evaluation of the
components of each tree, followed by pruning. The idea
behind this approach is to identify and eliminate rules
that cause misclassification. As a result we expect to
keep and generate rules that enhance the
classification. This method was tested on decision
trees generated by a genetic program whose aim was to
discover classification rules in financial stock
markets. From experimental results we can conclude that
our method is able to improve the accuracy and
precision of the classification.",
notes = "also known as \cite{1688571}
WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.",