An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

This paper presents some highlights in the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acqu isition. These techniques are capable of dealing with inexact and imprecise problem domains and have been demonstrated to be useful in the solution of classification problems. It addresses the issue of the application of appr opriate evaluation criteria such as rule base accuracy and comprehensibilit y for new knowledge acquisition techniques. An empirical study is then desc ribed in which three approaches to knowledge acquisition are investigated. The first approach combines neural networks and fuzzy logic, the second, ge netic algorithms and fuzzy logic, and in the third a rough sets approach ha s been examined, and compared. In this study neural network and genetic alg orithm fuzzy rule induction systems have been developed and applied to thre e classification problems. Rule induction software based on rough sets theo ry was also used to generate and test rule bases for the same data. A compa rison of these approaches with the C4.5 inductive algorithm was also carrie d out. Our research to date indicates that, based on the evaluation criteri a used, the genetic/fuzzy approach compares more than favourably with the n euro/fuzzy and rough set approaches. On the data sets used the genetic algo rithm system displays a higher accuracy of classification and rule base com prehensibility than the C4.5 inductive algorithm. (C) 1999 Elsevier Science B.V. All rights reserved.