2018/01/22 by Sibo Yang, Chao Zhang, Yang, Sibo +3
Computer Science · Engineering · Mathematics · #Category Theory (math.CT) #FOS: Computer and information sciences #FOS: Mathematics #Image Processing Techniques and Applications #Machine Learning (cs.LG) #Machine Learning and ELM #Neural Networks and Applications #cs.LG #math.CT
paper · pdf · doi:10.48550/arxiv.1801.07599
arxiv created 2018/01/22 · openalex publication_date 2018/01/22 · arxiv updated 2018/01/24 · openalex created_date 2018/02/02 · openalex updated_date 2026/07/28
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), 2q] 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.