A performance comparison of trained multilayer perceptrons and trained classification trees
Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics
Multi-layer perceptrons and trained classification trees are two very different techniaues which have recentlv become popuiar. Given enough data and time, both methods are capable of performing arbitrary non-linear classification. These two techniques, which developed out of different research communities, have not been previously compared on real-world problems. We first consider the important differences between multi-layer perceptrons and classification trees and conclude that there is not
... that there is not enough theoretical basis for the clear-cut superiority of one technique over the other. For this reason, we performed a number of empirical tests on quite different problems in power system load forecasting and speaker-independent vowel identification. We compared the performance for classification and prediction in terms of accuracy outside the training set. In all cases, even with various sizes of training sets, the multi-layer perceptron performed as well as or better than the trained classification trees. We are confident that the univariate version of the trained classification trees do not perform as well as the multi-layer perceptron. More studies are needed, however, on the comparative performance of the linear combination version of the classification trees.