Orthopartitions are partitions with uncertainty. We survey their use in knowledge representation (KR) and machine learning (ML). In particular, in KR their connection with possibility theory, intuitionistic fuzzy sets and credal partitions is discussed. As far as ML is concerned, their use in soft clustering evaluation and to define generalized decision trees are recalled. The (open) problem of relating an orthopartition to a partial equivalence relation is also sketched.

Ciucci, D., Boffa, S., Campagner, A. (2022). Orthopartitions in Knowledge Representation and Machine Learning. In Rough Sets - International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings (pp.3-18). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-21244-4_1].

Orthopartitions in Knowledge Representation and Machine Learning

Ciucci D.
;
Boffa S.;Campagner A.
2022

Abstract

Orthopartitions are partitions with uncertainty. We survey their use in knowledge representation (KR) and machine learning (ML). In particular, in KR their connection with possibility theory, intuitionistic fuzzy sets and credal partitions is discussed. As far as ML is concerned, their use in soft clustering evaluation and to define generalized decision trees are recalled. The (open) problem of relating an orthopartition to a partial equivalence relation is also sketched.
paper
Credal partition; Decision tree; Intuitionistic fuzzy sets; Orthopairs; Partial labels; Partition; Possibility theory; Rough sets; Soft clustering;
English
International Joint Conference on Rough Sets, IJCRS 2022 - 11 November 2022 through 14 November 2022
2022
Rough Sets - International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings
9783031212437
2022
13633
3
18
reserved
Ciucci, D., Boffa, S., Campagner, A. (2022). Orthopartitions in Knowledge Representation and Machine Learning. In Rough Sets - International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings (pp.3-18). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-21244-4_1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/424598
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