One-against-all multiclass classification based on multiple complementary neural networks

In general, there are two ways to deal with one-against-all multiclass neural network classification. The first way is the use of a single k-class neural network trained with multiple outputs. Another way is the use of multiple binary neural networks. This paper focuses on the later way in which multiple complementary neural networks are applied to one-against-all instead of using only multiple binary neural networks. We experiment our proposed technique using the glass data set which is one of the extremely unbalance data sets from the UCI machine learning repository. It is found that our approach improves classification performance when compared to the existing one-against-all techniques based on a single k-class neural network, a single k-class complementary neural networks, and multiple binary neural networks.