Thursday, September 26
5:00 pm 
Campus Center 120

An Empirical Evaluation of Bagging in Inductive Logic Programming
Inˆs de Castro Dutra

Visiting Assistant Professor
Department of Biostatistics and Medical Informatics
University of Wisconsin-Madison

Abstract

Ensembles have proven useful for a variety of applications, with a
variety of machine learning approaches.  While Quinlan has applied
boosting to FOIL, the widely-used approach of bagging has never been
employed in ILP.  Bagging has the advantage over boosting that the
different members of the ensemble can be learned and used in parallel.  
This advantage is especially important for ILP where run-times often are  
high.  We evaluate bagging on three different application domains using  
the complete-search ILP system, Aleph. We contrast bagging with an  
approach where we take advantage of the non-determinism in ILP search, by 
simply allowing Aleph to run multiple times, each time choosing ``seed''  
examples at random.