Machine Learning
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Ordering and Finding the Best of K2 Supervised Learning Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
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Most of the well-known ensemble techniques use the same training algorithm and the same sequence of patterns from the learning set to adapt the trainable parameters (weights) of the neural networks in the ensemble. In this paper, we propose to replace the traditional training algorithm in which the sequence of patterns is kept unchanged during learning. With the new algorithms we want to add diversity to the ensemble and increase its accuracy by altering the sequence of patterns for each concrete network. Two new training set reordering strategies are proposed: Static reordering and Dynamic reordering. The new algorithms have been successfully tested with six different ensemble methods and the results show that reordering is a good alternative to traditional training.