Evolutionary approaches to fuzzy modelling for classification
The Knowledge Engineering Review
A co-evolving decision tree classification method
Expert Systems with Applications: An International Journal
Application of wrapper approach and composite classifier to the stock trend prediction
Expert Systems with Applications: An International Journal
Quality management in GPRS networks with fuzzy case-based reasoning
Knowledge-Based Systems
Speed-up of the R4-rule for distance-based neural network learning
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Generating smart robot controllers through co-evolution
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
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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.