Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
Stacking with Multi-response Model Trees
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
| Hi-index | 0.00 |
An ensemble is a collective decision making system which applies some strategy to combine the predictions of classifiers to generate its prediction on new instances. Stacking is a well-known approach among the ensembles. It is not easy to find a suitable ensemble configuration for a specific dataset. Ant Colony Optimization (ACO) is a popular metaheuristic approach which could be a solution to find configurations. In this work, we propose a new Stacking construction method which applies ACO in the Stacking construction process to generate domain-specific configurations. The experiment results show that the new approach can achieve promising results on 18 datasets compared with some well-known ensemble approaches.