Combination of Neural Networks for Multi-label Document Classification

This paper deals with multi-label classification of Czech documents using several combinations of neural networks. It is motivated by the assumption that different nets can keep some complementary information and that it should be useful to combine them. The main contribution of this paper consists in a comparison of several combination approaches to improve the results of the individual neural nets. We experimentally show that the results of all the combination approaches outperform the individual nets, however they are comparable. However, the best combination method is the supervised one which uses a feed-forward neural net with sigmoid activation function.