The implication of our successful implementation of stacked generalization is that earlier model combination methods employing  weighted  majority vote  averaging  or other com putations that do not make use of level   learning  can now apply this learning to improve their predictive accuracy

## Acknowledgment

The authors are grateful to the New ealand Marsden Fund for financial support for this research This work was conducted when the first author was in Department of Computer Science  University of Waikato  The authors are grateful to    Ross Quinlan for providing and David W  Aha for providing IB    The anonymous reviewers and the editor have c provided many helpful comments

## References

Aha  D  W    D   Kibler     M  K   Albert chine Learning, 6 pp Instance Based Learning Algorithms  Ma-

Ali K M
M
azzani Error Reduction through Learning Multiple Descrip tions  Machine Learning Vol No p
Blake C E Keogh http ifornia  Department of Information and Computer Science

Merz UCI Repository of machine learning databases C

www ics uci edu mlearn MLRepository html Irvine CA University of Cal
Breiman  L
Stacked Regressions  Machine Learning  Vol a p Breiman  L
Bagging  redictors  Machine Learning  Vol No b p Breiman  L
Bias Variance and Arcing Classifiers  Technical Report 460  De C
partment of Statistics  University of California  Berkeley  CA
Breiman  L
H Friedman  R A  Olshen Stone Classification And Regresc sion Trees Belmont CA Wadsworth Estimating  robabilities  A Crucial Task in Machine Learning  In Cestnik B
Proceedings of the European Conference on Artificial Intelligence  pp Chan Stolfo A Comparative Evaluation of Voting and Meta learning S
K
on  artitioned Data  In Proceedings of the Twelfth International Conference on Machine Learning pp Morgan Kaufmann

Cost S
S Salzberg A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features  Machine Learning, 10 pp Fan  D W
K Chan S
Stolfo A Comparative Evaluation of Combiner and Stacked Generalization In Proceedings of AAAI-96 workshop on Integrating Multiple Learned Models pp
Neural Network Ensembles  IEEE Transactions of Hansen  L K
Salamon Pattern Analysis and Machine Intelligence, 12 pp