Data mining and machine learning: supporting labour inspectorates to address undeclared work
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Organización Internacional del Trabajo (OIT)
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| Format: | Reporte |
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International Labour Organization
2023
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| author | Ada Huibregtse (Author) Eleni Alogogianni (Author) |
| author_facet | Ada Huibregtse (Author) Eleni Alogogianni (Author) Ada Huibregtse (Author) Eleni Alogogianni (Author) |
| contents | 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 |
| format | Reporte |
| id | ilo_995351079302676 |
| institution | Organización Internacional del Trabajo (OIT) |
| publishDate | 2023 |
| publisher | International Labour Organization |
| spellingShingle | Data mining and machine learning: supporting labour inspectorates to address undeclared work Ada Huibregtse (Author) Eleni Alogogianni (Author) 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 |
| title | Data mining and machine learning: supporting labour inspectorates to address undeclared work |
| url | https://researchrepository.ilo.org/esploro/outputs/report/Data-mining-and-machine-learning-supporting/995351079302676 |