Towards Explainable Sequential Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912402591711232 |
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| author | Bergami, Giacomo Packer, Emma Scott, Kirsty Del Din, Silvia |
| author_facet | Bergami, Giacomo Packer, Emma Scott, Kirsty Del Din, Silvia |
| contents | This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible through a preliminary a posteriori explainable phase describing the numerical input data in terms of concurrent constituents with numerical payloads. This further required extending the event-based literature to design specification mining algorithms supporting concurrent constituents. Our previous and current solutions outperform state-of-the-art solutions for multivariate time series classifications, thus showcasing the effectiveness of the proposed methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Explainable Sequential Learning Bergami, Giacomo Packer, Emma Scott, Kirsty Del Din, Silvia Databases Artificial Intelligence This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible through a preliminary a posteriori explainable phase describing the numerical input data in terms of concurrent constituents with numerical payloads. This further required extending the event-based literature to design specification mining algorithms supporting concurrent constituents. Our previous and current solutions outperform state-of-the-art solutions for multivariate time series classifications, thus showcasing the effectiveness of the proposed methodology. |
| title | Towards Explainable Sequential Learning |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2505.23624 |