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

Alexandros Iosifidis, Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas

    Research output: Contribution to journalArticleScientificpeer-review

    17 Citations (Scopus)


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


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

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Software
    • General Neuroscience
    • Computer Networks and Communications
    • Artificial Intelligence


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