Today I read a paper titled “Rule-based Machine Learning Methods for Functional Prediction”
The abstract is:
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.