Data Fitting with Rule-Based Regression
Luís Torgo
1995
Abstract
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 R2. 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, R2 will out-perform regression
trees. The initial set of experiments that we have conducted in
artificial data sets show that R2 compares reasonably to other machine
learning systems.