Utilizando técnicas de mineração de dados para apoiar a busca ativa de famílias em situação de vulnerabilidade e risco social
In the current Brazilian Government there is a Social Assistance policy that is highly concerned about helping families who might be at social risk and vulnerability. The process of identification of these families is known as “active search”. The task of active search is defined in a document by th...
| Autor principal: | Terrin, Marcos Alexandre Pastori |
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| Formato: | Dissertação |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2018
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/2930 |
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| Resumo: |
In the current Brazilian Government there is a Social Assistance policy that is highly concerned about helping families who might be at social risk and vulnerability. The process of identification of these families is known as “active search”. The task of active search is defined in a document by the Brazilian Ministry of Social Development and Fight Against Hunger. This document provides the main guidelines about how to perform the active search. However, despite the task’s importance, there are still no tool to help the social assistants with this task. This work aim to investigate the use of data mining techniques to identify the families in vulnerability and social risk situations. The results obtained in preliminary experiments showed that the classification models created always predict the majority class. After balancing manually the datasets by removing some examples the experiments were repeated and showed that the results were being directly influenced by the imbalanced data. Because of it was used a bunch of sampling methods to produce the same amount of examples in each class. After proceed with the sampling of the examples new experiments were proceeded. During the result’s evaluation it was realized that the standard metrics used in machine learn were not being able to identify wich method obtained the best result. Due to this situation a ranking quality method was used combined with the Recall metric to evaluate the results. |
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