Data preprocessing to improve fairness in machine learning models: An application to the reintegration process of demobilized members of armed groups in Colombia

Alveiro Rosado Gomez; Maritza Liliana Calderón Benavides; Oscar Espinosa

doi:10.1016/j.asoc.2023.111193

Data preprocessing to improve fairness in machine learning models: An application to the reintegration process of demobilized members of armed groups in Colombia

Alveiro Rosado Gomez, Maritza Liliana Calderón Benavides, Oscar Espinosa

Research output: Articles / Notes › Scientific Article › peer-review

Original language	English
Journal	Applied Soft Computing
DOIs	https://doi.org/10.1016/j.asoc.2023.111193
State	Published - 1 Feb 2024

Research Areas UNAB

Sistemas de información e Ingeniería de software

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.asoc.2023.111193

Cite this

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title = "Data preprocessing to improve fairness in machine learning models: An application to the reintegration process of demobilized members of armed groups in Colombia",

author = "Gomez, {Alveiro Rosado} and Benavides, {Maritza Liliana Calder{\'o}n} and Oscar Espinosa",

year = "2024",

month = feb,

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doi = "10.1016/j.asoc.2023.111193",

language = "Ingl{\'e}s",

journal = "Applied Soft Computing",

issn = "1568-4946",

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TY - JOUR

T1 - Data preprocessing to improve fairness in machine learning models: An application to the reintegration process of demobilized members of armed groups in Colombia

AU - Gomez, Alveiro Rosado

AU - Benavides, Maritza Liliana Calderón

AU - Espinosa, Oscar

PY - 2024/2/1

Y1 - 2024/2/1

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DO - 10.1016/j.asoc.2023.111193

M3 - Artículo Científico

SN - 1568-4946

JO - Applied Soft Computing

JF - Applied Soft Computing

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