The mathematical foundations of learning machines
The mathematical foundations of learning machines
Reducing Communication for Distributed Learning in Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Class-switching neural network ensembles
Neurocomputing
Rectangular basis functions applied to imbalanced datasets
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Building ensembles of neural networks with class-switching
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
| Hi-index | 0.00 |
Boosting constructs a weighted classifier out of possibly weak learners by successively concentrating on those patterns harder to classify. While giving excellent results in many problems, its performance can deteriorate in the presence of patterns with incorrect labels. In this work we shall use parallel perceptrons (PP), a novel approach to the classical committee machines, to detect whether a pattern's label may not be correct and also whether it is redundant in the sense of being well represented in the training sample by many other similar patterns. Among other things, PP allow to naturally define margins for hidden unit activations, that we shall use to define the above pattern types. This pattern type classification allows a more nuanced approach to boosting. In particular, the procedure we shall propose, balanced boosting, uses it to modify boosting distribution updates. As we shall illustrate numerically, balanced boosting gives very good results on relatively hard classification problems, particularly in some that present a marked imbalance between class sizes.