Separate-and-conquer Regression
Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet, Kassel, Germany, (2010)Abstract
In this paper a rule learning algorithm for the prediction of a numerical target variable is presented. It is based on the separate-and-conquer strategy and the classification phase is done by a decision list. A new splitpoint generation method is introduced for the efficient handling of numerical attributes. It is shown that the algorithm performs comparable to other regression algorithms where some of them are based on rules and some are not. Additionally a novel heuristic for evaluating the trade-off between consistency and generality of regression rules is introduced. This heuristic features a parameter to directly trade off the rules consistency and its generality. We present an optimal setting for this parameter based on an optimization on several data sets.
