Learning non-overlapping rules A method based on
Functional Dependency Network and MDL Genetic
Programming
Proceedings of the 2006 IEEE Congress on Evolutionary
Computation, page 2717--2724. Vancouver, IEEE Press, (6-21 July 2006)Abstract
Classification rule is a useful model in data mining.
Given variable values, rules classify data items into
different classes. Different rule learning algorithms
are proposed, like Genetic Algorithm (GA) and Genetic
Programming (GP). Rules can also be extracted from
Bayesian Network (BN) and decision trees. However, all
of them have disadvantages and may fail to get the best
results. Both of GA and GP cannot handle cooperation
among rules and thus, the learnt rules are likely to
have many overlappings, i.e. more than one rules
classify the same data items and different rules have
different predictions. The conflicts among the rules
reduce their understandability and increase their usage
difficulty for expert systems. In contrast, rules
extracted from BN and decision trees have no
overlapping in nature. But BN can handle discrete
values only and cannot represent higher-order
relationships among variables. Moreover, the search
space for decision tree learning is huge and thus, it
is difficult to reach the global optimum. In this
paper, we propose to use Functional Dependency Network
(FDN) and MDL Genetic Programming (MDLGP) to learn a
set of non-overlapping classification rules 17. The
FDN is an extension of BN; it can handle all kind of
values; it can represent higher-order relationships
among variables; and its learning search space is
smaller than decision trees'. The experimental results
demonstrate that the proposed method can successfully
discover the target rules, which have no overlapping
and have the highest classification accuracies.
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