Binary Output Layer of Extreme Learning Machine for Solving Multi-class Classification Problems

Abstract

Considered in this paper is the design of output layer nodes of extreme learning machine (ELM) for solving multi-class classification problems with r ( $$r\ge 3$$ ) 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 ith class ( $$1\le i\le r$$ ), the ideal output is 1 for the ith output node, and 0 for all the other output nodes. We propose in this paper a new “binary approach”: Suppose $$2^{q-1}< r\le 2^q$$ with $$q\ge 2$$ , 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 and hidden-output weights than, the traditional one-to-one approach.