Combining locally trained neural networks by introducing a reject class

This paper presents a new strategy for building and combining a local committee when a dataset is given. Training local committees is performed in two stages: active data partitioning and recombination by introducing an additional reject class. Active data partitioning is a preprocessing step that partitions the given dataset into several similar subsets using active learning. Additional reject class in this strategy plays an important role in assigning a focused area to each individual network of the committee. For combining the outputs of each individual network, we use a kind of sum rule criteria, assuming that the outputs of the individuals are equivalent to a posteriori Bayesian probabilities. All the learning procedures are based on the active learning paradigm. Experiments are performed on the two real-world datasets from the UCI machine learning database. The results show that the active data partitioning and recombining strategy is very successful for building a local committee and the combined result outperforms other algorithms, but the combined result can be affected by the training error level /spl epsiv/.