Inproceedings,
Learning acyclic decision trees with Functional Dependency Network and MDL Genetic Programming
International Multi-Conference on Computing in the Global Information Technology, ICCGI '06, page 25. Bucharest, IEEE, (1-3 August 2006)
DOI: doi:10.1109/ICCGI.2006.46
Abstract
One objective of data mining is to discover
parent-child relationships among a set of variables in
the domain. Moreover, showing parents' importance can
further help to improve decision makings' quality.
Bayesian network (BN) is a useful model for multi-class
problems and can illustrate parent-child relationships
with no cycle. But it cannot show parents' importance.
In contrast, decision trees state parents' importance
clearly, for instance, the most important parent is put
in the first level. However, decision trees are
proposed for single-class problems only, when they are
applied to multi-class ones, they are likely to produce
cycles representing tautologic. In this paper, we
propose to use MDL genetic programming (MDLGP) and
functional dependency network (FDN) to learn a set of
acyclic decision trees (Shum et al., 2005). The FDN is
an extension of BN; it can handle all of discrete,
continuous, interval and ordinal values; it guarantees
to produce decision trees with no cycle; its learning
search space is smaller than decision trees'; and it
can represent higher-order relationships among
variables. The MDLGP is a robust genetic programming
(GP) proposed to learn the FDN. We also propose a
method to derive acyclic decision trees from the FDN.
The experimental results demonstrate that the proposed
method can successfully discover the target decision
trees, which have no cycle and have the accurate
classification results
