TY  - RPRT
ID  - TUD-KE-2010-05
T1  - Rule Stacking: An approach for compressing an ensemble of rule sets into a single classifier
A1  - Sulzmann, Jan-Nikolas
A1  - Fürnkranz, Johannes
Y1  - 2010
IS  - TUD-KE-2010-05
T2  - TU Darmstadt, Knowledge Engineering Group
UR  - /publications/reports/tud-ke-2010-05.pdf
N2  - In this paper, we present an approach for compressing a rule-based pairwise classifier ensemble into a single rule set that
can be directly used for classification. The key idea is to re-encode the training examples using information about which
of the original ruler covers the example, and to use them for training a rule-based meta-level classifier. We not only show
that this approach is more accurate than using the same classifier at the base level (which could have been expected for
such a variant of stacking), but also demonstrate that the resulting meta-level rule set can be straight-forwardly translated
back into a rule set at the base level. Our key result is that the rule sets obtained in this way are of comparable complexity
to those of the original rule learner, but considerably more accurate.
ER  -