Decision tree classification provides a rapid and effective method of categorising datasets. Many algorithmic methods exist for optimising decision tree structure, although these can be vulnerable to changes in the training dataset. An evolutionary method is presented which allows decision tree flexibility through the use of co-evolving competition between the decision tree and the training data set. This method is tested using two different datasets and gives results comparable with or superior to other classification methods. A final discussion argues for the utility of decision trees over algorithmic or other alternative methods such as neural networks, particularly in situations where a large number of variables are being considered.
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