Release Time:2020-09-06  Hits:

Indexed by: Journal Papers

Date of Publication: 2020-08-01

Journal: NEURAL PROCESSING LETTERS

Included Journals: SCIE

Volume: 52

Issue: 1,SI

Page Number: 153-167

ISSN: 1370-4621

Key Words: Extreme learning machines (ELM); Multi-class classification problems; One-to-one approach; Binary approach; Accuracies

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 >= 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 <= i <= 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 "binar y approach": Suppose 2(q-1) < r <= 2(q) 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 and hidden-output weights than, the traditional one-to-one approach.