In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges can be encoded with real-valued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system. I. Introduction Genetic algorithms (GAs) have proved to be robust, domain independent mechanisms for numeric and symbolic optimization[7]. Our previous work has demonstrated effective genetic-based rule learning in discrete domains [13]. In the real world, however, most classification prob...

We report on an extensive simulation study comparing eight statistical classification methods, focusing on problems where the number of observations is less than the number of variables. Using a wide range of artificial and real data, two types of classifiers were contrasted; methods that classify using all variables, and methods that first reduce the number of dimensions to two or three. The full feature space methods include linear, quadratic and regularized discriminant analysis, and the nearest neighbour method. The four dimensionality reducing classifiers are characterized by the transform they implement. The four transforms compared are the Fisher discriminant plane, the Fisher-Fukunaga-Koonz, the Fisher-radius, and the Fisher-variance transforms. The FisherFukunaga and the Fisher-radius transform based classifiers have recently been proposed for two class classification problems. We also present an extension to these transforms such that they can be applied to classification pro...