Data Fitting with Rule-based Regression

In the classical regression theory we try to build one functional model to fit a set of data. In noisy and complex domains this methodology can be highly unreliable and/or demand too complex functional models. Piecewise regression models provide means to overcome these difficulties. Some existing approaches to piecewise regression are based on regression trees. However, rules are known to be more powerful descriptive languages than trees. This paper describes the rule learning system R 2 . This system learns a set of regression rules from a classical machine learning data set. Regression rules are IF-THEN rules that have regression models in the conclusion. The conditional part of these rules determines the domain of applicability of the respective model. We believe that by adopting a rule-based formalism, R 2 will out-perform regression trees. The initial set of experiments that we have conducted in artificial data sets show that R 2 compares reasonably to other machine learning systems.