Title :
Using real-valued genetic algorithms to evolve rule sets for classification
Author :
Corcoran, Arthur L. ; Sen, Sandip
Author_Institution :
Dept. of Math. & Comput. Sci., Tulsa Univ., OK, USA
Abstract :
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 could 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
Keywords :
classification; genetic algorithms; learning (artificial intelligence); pattern recognition; classification; genetic algorithms; machine learning system; optimization problem; real-valued attribute; real-valued attributes; real-valued genes; rule sets; supervised classification; Application software; Costs; Educational institutions; Fault detection; Genetic algorithms; Induction generators; Learning systems; Machine learning; Medical diagnosis; Robustness;
Conference_Titel :
Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1899-4
DOI :
10.1109/ICEC.1994.350030
