A genetic prototype learner
Sandip Sen, Leslie Knight · 1995
Supervised classification problems have received considerable attention from the machine learning community. We propose a novel genetic algorithm based prototype learning system, PLEASE, for this class of problems. Given a set of prototypes for each of the possible classes, the class of an input instance is determined by the prototype nearest to this instance. We assume ordinal attributes and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space as determined by corresponding feature-value pairs. Comparisons with C4.5 on a set of artificial problems of controlled complexity demonstrate the effectiveness of the proposed system. 1 Introduction The induction of concept classification methods has received widespread attention both in the cognitive sciences and in the machine learning communities. This is to be expected because the development and use of concepts is a key c...