Neural implementation of tree classifiers

Tree classifiers represent a popular non-parametric classification methodology that has been successfully used in many pattern recognition and learning tasks. However, "is feature-value/spl ges/thrsh" type of tests used in tree classifiers are often found sensitive to noise and minor variations in the data. This has led to the use of soft thresholding in decision trees. Following the decision tree to feedforward neural network mapping of the entropy net, three neural implementation schemes for tree classifiers, that allow soft thresholding, are presented in this paper. Results of several experiments using well-known data sets are described to compare the performance of the proposed implementations with respect to decision trees with hard thresholding. >