Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Reporte |
| Veröffentlicht: |
International Labour Organization
2023
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| Online-Zugang: | https://researchrepository.ilo.org/esploro/outputs/report/Data-mining-and-machine-learning-supporting/995351079302676 |
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Inhaltsangabe:
- Data mining and machine learning: supporting labour inspectorates to address undeclared work Ada Huibregtse (Author) Eleni Alogogianni (Author) This paper focuses on revealing the superior predictive power of Data mining & Machine learning approaches – compared to manually configured red-flag approaches – in targeting businesses for inspections and in increasing labour inspection’s efficiency in uncovering undeclared work. Unlike manually set red-flag methods, DM&ML tools increase the predictive accuracy by, first, automatically considering variables that traditional paradigms will omit about who, how and why one engages in undeclared work or other labour law violations; and second, by identifying changes in behavioural patterns significantly faster than experts or practitioners. ILO publication.report