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Orthopartitions in Knowledge Representation and Machine Learning

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Rough Sets (IJCRS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13633))

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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.

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Authors and Affiliations

  1. Università degli Studi di Milano-Bicocca, DISCo, Viale Sarca 336, 20126, Milano, Italy

    Davide Ciucci, Stefania Boffa & Andrea Campagner

Authors
  1. Davide Ciucci
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  2. Stefania Boffa
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  3. Andrea Campagner
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Corresponding author

Correspondence to Davide Ciucci .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    JingTao Yao

  2. Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  3. Shanghai University, Shanghai, China

    Xiaodong Yue

  4. Tongji University, Shanghai, China

    Duoqian Miao

  5. University of Kansas, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  6. Soochow University, Suzhou, Jiangsu, China

    Fanzhang Li

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Ciucci, D., Boffa, S., Campagner, A. (2022). Orthopartitions in Knowledge Representation and Machine Learning. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-21244-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21243-7

  • Online ISBN: 978-3-031-21244-4

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