| Title of paper:
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On OWA, Machine Learning and Big Data: The case for IFS over universes
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| Author(s):
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| Panagiotis Chountas
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| School of Computer Science & Engineering, University of Westminster, 15 New Cavendish Street, London W1W 6UW, London, United Kingdom
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| chountp@westminster.ac.uk
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| Mustafa Hajmohammed
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| School of Computer Science & Engineering, University of Westminster, 15 New Cavendish Street, London W1W 6UW, London, United Kingdom
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| m.hajmohammed@westminster.ac.uk
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| Ismael Rhemat
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| School of Computer Science & Engineering, University of Westminster, 15 New Cavendish Street, London W1W 6UW, London, United Kingdom
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| i.rhemat@westminster.ac.uk
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| Presented at:
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Proceedings of the 27th International Conference on Intuitionistic Fuzzy Sets, 5–6 July 2024, Burgas, Bulgaria
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| Published in:
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Notes on Intuitionistic Fuzzy Sets, Volume 30 (2024), Number 2, pages 113–120
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| DOI:
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https://doi.org/10.7546/nifs.2024.30.2.113-120
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| Download:
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 PDF (1168 Kb, Info)
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| Abstract:
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This paper provides a holistic view of open-world machine learning by investigating class discovery, and class incremental learning under OWA. The challenges, principles, and limitations of current methodologies are discussed in detail. Finally, we position IFS over multiple universes as a formalism to capture the evolution in Big Data as part of incremental learning.
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| Keywords:
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Intuitionistic fuzzy sets, Big data, Incremental learning, Machine learning.
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| AMS Classification:
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03E72, 68T05.
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| References:
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