Abstract

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance. 1. Introduction The problem of approximating the values of a continuous variable is described in the statistical literature as regression. Given samples of output (response) variable y and input (predictor) variables x = fx 1 :::x n g, the regression task is to find a mapping y = f(x). Relative to the spac...

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