Towards a Logic-Based View of Some Approaches to Classification Tasks
Résumé
This paper is a plea for revisiting various existing approaches to the handling of data, for classification purposes, based on a set-theoretic view, such as version space learning, formal concept analysis, or analogical proportion-based inference, which rely on different paradigms and motivations and have been developed separately. The paper also exploits the notion of conditional object as a proper tool for modeling if-then rules. It also advocates possibility theory for handling uncertainty in such settings. It is a first, and preliminary, step towards a unified view of what these approaches contribute to machine learning.
Mots clés
Data
Classification
Version space
Conditional object If-then rule
Analogical proportion
Formal concept analysis
Possibility theory
Possibilistic logic
Bipolarity
Uncertainty
data
classification
version space
conditional object
if-then rule
analogical proportion
formal concept analysis
possibility theory
possibilistic logic
bipolarity
uncertainty
Domaines
Intelligence artificielle [cs.AI]
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