Binary output layer of feedforward neural networks for solving multi-class classification problems
This is an incremental improvement for neural network practitioners, offering a more efficient output layer design for multi-class classification problems.
The paper tackles multi-class classification by proposing a binary output layer design for feedforward neural networks, which reduces the number of output nodes compared to the conventional one-to-one approach while achieving similar performance in numerical experiments.
Considered in this short note is the design of output layer nodes of feedforward neural networks for solving multi-class classification problems with r (bigger than or equal to 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, 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 r is (2^(q minus 1), 2^q] with q bigger than or equal to 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.