A Genetic Prototype Learner

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 characteristic of intelligent behavior. A study of the acquisition of conceptual knowledge involves an analysis of the representation of this knowledge, as well as the process of drawing inductive inferences using these representations. A number of symbolic and subsymbolic systems have been proposed to solve the problem of inductively acquiring concept classification knowledge from a set of pre-classified instances. Some of the more influential The most widely used subsymbolic systems for concept learning are based either on neural networks [Rumel-hart et ai, 1986] or on genetic algorithms (GAs) [De-Jong, 1990; Holland, 1986]. Some of the more frequently used representations in symbolic concept learning systems use logic expressions and programs, decision trees and lists, hierarchical clusters, and rules. Representations used in subsymbolic systems include networks of simple computational units connected by weighted links and bit strings. In addition to these mainstream machine learning approaches , there exists a significant body of work on statistical induction schemes [Breiman et ai, 1984; Cheese-man et ai, 1988]. Other researchers have been motivated by the findings of cognitive psychologists These methods store the set of training instances and classify a new instance by some voting scheme using a given number of nearest stored examples. The exemplar-based approach (also called the nearest neighbor classification algorithm [Vosniadou and Ortony, 1989]) is attractive because of its simple representation, and because it can be used to explain a number of human cognitive phenomena [Schaffer, 1978; Smith and Medin, 1981]. Hybrid GA-nearest neighbor algorithms have been developed to learn …