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
- Uncertainty
- Data
- Classification
- Version space
- Conditional object If-then rule
- Analogical proportion
- Formal concept analysis
- Possibility theory
- Possibilistic logic
- Bipolarity
- possibility theory
- data
- classification
- version space
- conditional object
- if-then rule
- analogical proportion
- formal concept analysis
- uncertainty
- bipolarity
- possibilistic logic
Domaines
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