Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910095196028928 |
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| author | Frizzo, Davide Borsatti, Francesco Arcudi, Alessio De Moliner, Antonio Oboe, Roberto Susto, Gian Antonio |
| author_facet | Frizzo, Davide Borsatti, Francesco Arcudi, Alessio De Moliner, Antonio Oboe, Roberto Susto, Gian Antonio |
| contents | Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01158 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI Frizzo, Davide Borsatti, Francesco Arcudi, Alessio De Moliner, Antonio Oboe, Roberto Susto, Gian Antonio Machine Learning Artificial Intelligence Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations. |
| title | Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.01158 |