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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.15012 |
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| _version_ | 1866910763734532096 |
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| author | Boersma, Marcel Manoorkar, Krishna Palmigiano, Alessandra Panettiere, Mattia Tzimoulis, Apostolos Wijnberg, Nachoem |
| author_facet | Boersma, Marcel Manoorkar, Krishna Palmigiano, Alessandra Panettiere, Mattia Tzimoulis, Apostolos Wijnberg, Nachoem |
| contents | The framework developed in the present paper provides a formal ground to generate and study explainable categorizations of sets of entities, based on the epistemic attitudes of individual agents or groups thereof. Based on this framework, we discuss a machine-leaning meta-algorithm for outlier detection and classification which provides local and global explanations of its results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Flexible categorization using formal concept analysis and Dempster-Shafer theory Boersma, Marcel Manoorkar, Krishna Palmigiano, Alessandra Panettiere, Mattia Tzimoulis, Apostolos Wijnberg, Nachoem Artificial Intelligence The framework developed in the present paper provides a formal ground to generate and study explainable categorizations of sets of entities, based on the epistemic attitudes of individual agents or groups thereof. Based on this framework, we discuss a machine-leaning meta-algorithm for outlier detection and classification which provides local and global explanations of its results. |
| title | Flexible categorization using formal concept analysis and Dempster-Shafer theory |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2408.15012 |