Binary output layer of feedforward neural networks for solving multi-class classification problems

Considered in this short note is the design of output layer nodes of feedforward neural networks for solving multi-class classification problems with r (r ≥ 3) classes of samples. The common and conventional setting of output layer, called “one-to-one approach” in this paper, is as follows: The output layer contains r output nodes corresponding to the r classes. And for an input sample of the i-th class (1 ≤ i ≤ r), the ideal output is 1 for the i-th output node, and 0 for all the other output nodes. We propose in this paper a new “binary approach”: Suppose 2q−1 < r ≤ 2q with q ≥ 2, then we let the output layer contain q output nodes, and let the ideal outputs for the r classes be designed in a binary manner. Numerical experiments carried out in this paper show that our binary approach does equally good job as, but uses less output nodes than, the traditional one-to-one approach.