This dissertation develops knowledge-based feature generation, a stronger, but more restricted, method of constructive induction than was available previously. Knowledge-based feature generation is a heuristic method of using one general and easily available form of domain knowledge to create functional features for one class of learning problems. The method consists of heuristics for creating features, for pruning useless new features, and for estimating feature cost. It has been tested empirically on problems ranging from simple to complex, and with inductive learning algorithms of varying power. The results show knowledge-based feature generation to be a general method of creating useful new features for one class of learning problems.
