A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912083427196928 |
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| author | Thompson, Horacio Errecalde, Marcelo |
| author_facet | Thompson, Horacio Errecalde, Marcelo |
| contents | The eRisk laboratory aims to address issues related to early risk detection on the Web. In this year's edition, three tasks were proposed, where Task 2 was about early detection of signs of anorexia. Early risk detection is a problem where precision and speed are two crucial objectives. Our research group solved Task 2 by defining a CPI+DMC approach, addressing both objectives independently, and a time-aware approach, where precision and speed are considered a combined single-objective. We implemented the last approach by explicitly integrating time during the learning process, considering the ERDEθ metric as the training objective. It also allowed us to incorporate temporal metrics to validate and select the optimal models. We achieved outstanding results for the ERDE50 metric and ranking-based metrics, demonstrating consistency in solving ERD problems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_17963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024 Thompson, Horacio Errecalde, Marcelo Computers and Society Computation and Language Machine Learning The eRisk laboratory aims to address issues related to early risk detection on the Web. In this year's edition, three tasks were proposed, where Task 2 was about early detection of signs of anorexia. Early risk detection is a problem where precision and speed are two crucial objectives. Our research group solved Task 2 by defining a CPI+DMC approach, addressing both objectives independently, and a time-aware approach, where precision and speed are considered a combined single-objective. We implemented the last approach by explicitly integrating time during the learning process, considering the ERDEθ metric as the training objective. It also allowed us to incorporate temporal metrics to validate and select the optimal models. We achieved outstanding results for the ERDE50 metric and ranking-based metrics, demonstrating consistency in solving ERD problems. |
| title | A Time-Aware Approach to Early Detection of Anorexia: UNSL at eRisk 2024 |
| topic | Computers and Society Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.17963 |