A performance comparison of trained multilayer perceptrons and trained classification trees

Abstract

The important differences between multilayer perceptrons and classification trees are considered. A number of empirical tests on three real-world problems in power-system load forecasting, power-system security prediction, and speaker-independent vowel recognition are presented. The load-forecasting problem, which is partially a regression problem, uses past trends to predict the critical needs of future power generation. The power-security problem uses the classifier as an interpolator of previously known states of the system. The vowel-recognition problem is representative of the difficulties in automatic speech recognition caused by variability across speakers and phonetic context. In all cases even with various sizes of training sets, the multilayer perceptron performed as well as or better than the trained classification trees. It is therefore concluded that there is not enough theoretical basis to demonstrate clear-cut superiority of one technique over the other. >