Generation of comprehensible decision trees through evolution of training data

In machine learning, decision trees (DTs) are usually considered comprehensible because a reasoning process can be given for each conclusion. When the data set is large, however, the DTs obtained may become very large, and they are no longer comprehensible. To increase the comprehensibility of DTs, we have proposed several methods. For example, we have tried to evolve DTs using genetic programming (GP), with tree size as the secondary fitness measure; we have tried to initialize GP using results obtained by C4.5; and we have also tried to introduce the divide-and-conquer concept in GP, but all results obtained are still not good enough. Up to now we have tried to design good DTs from given fixed data. In this paper, we look at the problem from a different point of view. The basic idea is to evolve a small data set that can cover the domain knowledge as good as possible. From this data set, a small but good DT can be designed. The validity of the new algorithm is verified through several experiments