Discovering interesting classification rules with
genetic programming
Applied Soft Computing, 1 (4):
257--269 (May 2001)DOI: doi:10.1016/S1568-4946(01)00024-2
Abstract
Data mining deals with the problem of discovering
novel and interesting knowledge from large amount of
data. This problem is often performed heuristically
when the extraction of patterns is difficult using
standard query mechanisms or classical statistical
methods. In this paper a genetic programming framework,
capable of performing an automatic discovery of
classification rules easily comprehensible by humans,
is presented. A comparison with the results achieved by
other techniques on a classical benchmark set is
carried out. Furthermore, some of the obtained rules
are shown and the most discriminating variables are
evidenced.