Learning classifier systems for data mining: a comparison of XCS with other classifiers for the Forest Cover data set

This paper compares the performance, in terms of prediction accuracy, of a learning classifier system based on Wilson's XCS with commonly used classifiers from the fields of decision trees, neural networks and support vector machines. The experiments are performed on the Forest Cover Type database, a large data set available at the UCI KDD Archive. The first objective of this paper is to highlight the potential of XCS as a data mining tool. The second objective is to provide extensive benchmarking results for experiments performed under randomised conditions for several modelling techniques. We find that C5 Decision trees perform significantly better than other techniques, and that the learning classifier system performs better or as well as three of the eight classifiers used. We discuss why C5 outperforms the other classifiers and identify ways in which XCS could be adapted to make it more suitable for data mining.