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Closure operators: Complexity and applications to classification and decision-making

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  • Hamed Hamze Bajgiran
  • Federico Echenique

Abstract

We study the complexity of closure operators, with applications to machine learning and decision theory. In machine learning, closure operators emerge naturally in data classification and clustering. In decision theory, they can model equivalence of choice menus, and therefore situations with a preference for flexibility. Our contribution is to formulate a notion of complexity of closure operators, which translate into the complexity of a classifier in ML, or of a utility function in decision theory.

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  • Hamed Hamze Bajgiran & Federico Echenique, 2022. "Closure operators: Complexity and applications to classification and decision-making," Papers 2202.05339, arXiv.org, revised May 2022.
    • Handle: RePEc:arx:papers:2202.05339
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    References listed on IDEAS

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