One-class classification with extreme learning machine
type: Item
instance of: scholarly article
title: One-class classification with extreme learning machine
zbMATH Open document ID: 1394.68297
DOI: 10.1155/2015/412957
author: Qian Leng
author: Honggang Qi
author: Jun Miao
author: Guiping Su
author: Wen-Tao Zhu
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6926297
describes a project that uses: UCI-ml
describes a project that uses: LIBSVM
describes a project that uses: dd_tools
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/412957
OpenAlex ID: W2116607081
Wikidata QID: Q59118513
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description: scientific article; zbMATH DE number 6926297