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

Hamed Hamze Bajgiran and Federico Echenique

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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.

Date: 2022-02, Revised 2022-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dcm and nep-upt
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