Öznitelik seçimi ve makine öğrenmesi ile yoksulluk seviye karakterizasyonu

Targeting poverty requires access to accurate, timely and reliable quantitative data on socio-economic characteristics of households. However, in many developing countries, collecting accurate, timely, and reliable data on household characteristics is expensive, time-consuming, and unreliable, often requiring long and detailed surveys. Reliable data on economic status remain scarce in developing countries, hampering efforts to study these outcomes and to design appropriate policy responses to improve household welfare. In such situations machine learning algorithms can be of a great help. However, these models are normally designed in the form of black boxes; if the model is trained on a certain known data and predicted on unseen data, it doesn't give any information about the features that discriminate between classes. In other words, it is very tough to extract the features indicating that someone falls under specific category of poverty. Moreover, in poverty identification, measurement or classification, it is crucial to know how such features contribute to each class of poverty. Therefore, we designed an approach that extracts a subset of features that best characterize each poverty class, examines how this subset affect the chosen class and finally employ ensemble models to best classify between these classes. Through this approach we look at poverty from a multidimensional perspective contrary to a single dimension perspective defined as living on consumption expenditure of less than a predefined income threshold. The application and usefulness of our proposed framework is tested on a Costa Rican dataset collected from Kaggle website and provided by Inter-American Development Bank. Keywords: Poverty Characterization, Poverty Measurement, Poverty Identification, Multidimensional Poverty, Feature Extraction, Machine Learning.