ADAPTIVE CONCEPT-LEARNING ALGORITHM

This paper describes a concept learning algorithm call RF4 which adaptively improves its concept learning efficiency. RF4 performs a depth-first search on the basis of five criteria for pruning undesirable formulae and five transformation rules for combining formulae. The attribute search order is determined dynamically by estimating each attribute's probability of being a component of the concept. In Bongard problems, RF4 solved 41 out of 100 problems efficiently, while other existing learning algorithms solved much less. After solving the 41 training problems, the average time for RF4 to solve the same set of problems was reduced to about one-third.