Neural units recruitment algorithm for generation of decision trees
Guillaume Deffuant · 1990
The neural units recruitment algorithms (NEURAL) are algorithms mixing techniques from neural networks and symbolic machine learning. The size and architecture of the network are not specified before the learning process. Basic cells (perceptron units or delta rule units) are recruited and organized in a structure similar to the one of a decision tree which grows during the learning process (beginning with only one initial cell). As the tree grows, the leaf cells are specialized in smaller and smaller parts of the initial training set. Convergence is guaranteed for any set of patterns, with real inputs and Boolean outputs. Learning can be incremental: learning a new pattern set only alters the parts of the structure concerned with the differences between the old and the new sets