Comparison of three classification techniques: CART, C4.5 and Multi-Layer Perceptrons

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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A. C. Tsoi, R. Pearson


In this paper, after some introductory remarks into the classification prob(cid:173) lem as considered in various research communities, and some discussions concerning some of the reasons for ascertaining the performances of the three chosen algorithms, viz., CART (Classification and Regression Tree), C4.5 (one of the more recent versions of a popular induction tree tech(cid:173) nique known as ID3), and a multi-layer perceptron (MLP), it is proposed to compare the performances of these algorithms under two criteria: classi(cid:173) fication and generalisation. It is found that, in general, the MLP has better classification and generalisation accuracies compared with the other two algorithms.