Towards a logic-based view of some approaches to classification tasks. (English) Zbl 1512.68272
Lesot, Marie-Jeanne (ed.) et al., Information processing and management of uncertainty in knowledge-based systems. 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15–19, 2020. Proceedings, Part III. Cham: Springer. Commun. Comput. Inf. Sci. 1239, 697-711 (2020).
Summary: 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.
For the entire collection see [Zbl 1481.68017].
For the entire collection see [Zbl 1481.68017].
MSC:
68T05 | Learning and adaptive systems in artificial intelligence |
68T27 | Logic in artificial intelligence |
68T30 | Knowledge representation |
68T37 | Reasoning under uncertainty in the context of artificial intelligence |