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One-Class Classification based on Extreme Learning and Geometric Class Information

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalNeural Processing Letters
Publication statusPublished - 2016
Publication typeA1 Journal article-refereed


In this paper, we propose an extreme learning machine (ELM)-based one-class classification method that exploits geometric class information. We formulate the proposed method to exploit data representations in the feature space determined by the network hidden layer outputs, as well as in ELM spaces of arbitrary dimensions. We show that the exploitation of geometric class information enhances performance. We evaluate the proposed approach in publicly available datasets and compare its performance with the recently proposed one-class extreme learning machine algorithm, as well as with standard and recently proposed one-class classifiers. Experimental results show that the proposed method consistently outperforms the remaining approaches.


  • Big data, Extreme learning machine, Novelty detection, One-class classification

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