A SMALLEST GENERALIZATION STEP STRATEGY

Abstract * Over-generalization is a well-known problem in empirical learning. Incremental and prudent generalization is a means to avoid it. This is not always sufficient. The language in which the concepts are described may be incomplete, so that there is no conjunction to express a concept that is consistent with all the examples. This paper presents an interactive incremental learning method that generalizes in such a way that it is able to efficiently assist an user in locating the insufficiencies of the language and in correcting them whenever an over-generalization occurs. The generalization algorithm is based on a smallest generalization step strategy that determines processing order of the examples and the successive hypothesis to study.