Breeding Decision Trees Using Evolutionary Techniques
Athanassios Papagelis, Dimitris Kalles · 2001
We explore the use of genetic algorithms to directly evolve classification decision trees. We argue on the suitability of such a concept learner due to its ability to efficiently search complex hypotheses spaces and discover conditionally dependent as well as irrelevant attributes. The performance of the system is measured on a set of artificial and standard discretized concept-learning problems and compared with the performance of two known algorithms (C4.5, OneR). We demonstrate that the derived hypotheses of standard algorithms can substantially deviate from the optimum. This deviation is partly because of their non-universal procedural bias and it can be reduced using global metrics of tree quality like the one proposed. 1