Machine Learning
Ordering and Finding the Best of K2 Supervised Learning Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Decorrelated and CELS are two ensembles that modify the learning procedure to increase the diversity among the networks of the ensemble. Although they provide good performance according to previous comparatives, they are not as well known as other alternatives, such as Bagging and Boosting, which modify the learning set in order to obtain classifiers with high performance. In this paper, two different procedures are introduced to Decorrelated and CELS in order to modify the learning set of each individual network and improve their accuracy. The results show that these two ensembles are improved by using the two proposed methodologies as specific set generators.