Despite the rapid development in very recent years of Artificial Intelligence models to predict poverty, this problem still remains an unsolved open issue especially in a multidimensional perspective. In this work we present our proposal to face multidimensional poverty in case of a fragile population, the older adults, starting from an unlabelled dataset, collected administering a proper questionnaire to about 500 individuals. Firstly a model that allows to label the collected data into three classes of poverty is proposed. Then, XGBoost and Naive Bayes classifiers are considered to solve the classification problem. Finally, after having determined the relative importance of each feature, a novel Naive Bayes model is proposed that relies on new aggregated features that represent five poverty dimensions. These aggregated features are obtained by properly combining the variables collected through the questionnaire with cut-offs defined by a domain expert.
Olearo, L., D'Adda, F., Messina, V., Cremaschi, M., Bandini, S., Gasparini, F. (2023). An Artificial Intelligence approach to predict mutidimensional poverty of older people from unlabelled data. In Proceedings of the 4th Italian Workshop on Artificial Intelligence for an Ageing Society co-located with 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) (pp.98-112). CEUR-WS.
An Artificial Intelligence approach to predict mutidimensional poverty of older people from unlabelled data
Olearo, L;D'Adda, F;Messina, V;Cremaschi, M;Bandini, S;Gasparini, F
2023
Abstract
Despite the rapid development in very recent years of Artificial Intelligence models to predict poverty, this problem still remains an unsolved open issue especially in a multidimensional perspective. In this work we present our proposal to face multidimensional poverty in case of a fragile population, the older adults, starting from an unlabelled dataset, collected administering a proper questionnaire to about 500 individuals. Firstly a model that allows to label the collected data into three classes of poverty is proposed. Then, XGBoost and Naive Bayes classifiers are considered to solve the classification problem. Finally, after having determined the relative importance of each feature, a novel Naive Bayes model is proposed that relies on new aggregated features that represent five poverty dimensions. These aggregated features are obtained by properly combining the variables collected through the questionnaire with cut-offs defined by a domain expert.
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