An empirical evaluation of bagging in inductive logic programming

Authors:
Inês De Castro Dutra;David Page;Vítor Santos Costa;Jude Shavlik
Affiliations:
Department of Biostatistics and Medical Informatics and Department of Computer Sciences, University of Wisconsin-Madison;Department of Biostatistics and Medical Informatics and Department of Computer Sciences, University of Wisconsin-Madison;Department of Biostatistics and Medical Informatics and Department of Computer Sciences, University of Wisconsin-Madison;Department of Biostatistics and Medical Informatics and Department of Computer Sciences, University of Wisconsin-Madison
Venue:
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Year:
2002

Citing 12
Cited 9

Synergy of clustering multiple back propagation networks

Advances in neural information processing systems 2
Stacked regressions

Machine Learning
Bagging predictors

Machine Learning
Robust classification systems for imprecise environments

AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP

Data Mining and Knowledge Discovery
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Carcinogenesis Predictions Using ILP

ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Boosting First-Order Learning

ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Managing Network Resources in Condor

HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Tractable induction and classification in first order logic via stochastic matching

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Lattice-search runtime distributions may be heavy-tailed

ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

First order random forests: Learning relational classifiers with complex aggregates

Machine Learning
Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves

Machine Learning
Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble

ECML '07 Proceedings of the 18th European conference on Machine Learning
Exploiting propositionalization based on random relational rules for semi-supervised learning

PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Seeing the forest through the trees: learning a comprehensible model from a first order ensemble

ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Boosting first-order clauses for large, skewed data sets

ILP'09 Proceedings of the 19th international conference on Inductive logic programming
From inductive logic programming to relational data mining

JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
An integrated approach to learning bayesian networks of rules

ECML'05 Proceedings of the 16th European conference on Machine Learning
Automatic traffic incident detection based on nFOIL

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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.