Binary Output Layer of Extreme Learning Machine for Solving Multi-class Classification Problems
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论文类型：期刊论文
发表时间：2020-08-01
发表刊物：NEURAL PROCESSING LETTERS
收录刊物：SCIE
卷号：52
期号：1,SI
页面范围：153-167
ISSN号：1370-4621
关键字：Extreme learning machines (ELM); Multi-class classification problems; One-to-one approach; Binary approach; Accuracies
摘要：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.
发表时间：2020-08-01