Rule generation from real data: GAR meets LINNEO+

Abstract

In this paper we discuss our approach to learning classification rules from data. We sketch out two modules of our architecture, namely LINNEO+ and GAR. LINNEO+, which is a knowledge acquisition tool for ill-structured domains automatically generating classes from examples that incrementally works with an unsupervised strategy. LINNEO+'s output, a representation of the conceptual structure of the domain in terms of classes, is the input to GAR that is used to generate a set of classification rules for the original training set. GAR can generate both conjunctive and disjunctive rules. Herein we present an application of these techniques to data obtained from a real wastewater treatment plant in order to help the construction of a rule base. This rulebase will be used for a knowledge-based system that aims to supervise the whole process.