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Rule-based Machine Learning Methods for Functional Prediction

S. M. Weiss·N. Indurkhya
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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.

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BibTeXRIS

S. M. Weiss, N. Indurkhya. 1995-12-01. Rule-based Machine Learning Methods for Functional Prediction. https://arxiv.org/abs/cs/9512107

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