| From 69,130 households that were covered by a comprehensive community-based monitoring survey conducted in the Philippines, a neural network technique is used to identify the 'absolute poor' - those households whose per capita income is less that 1USD per day, based on the UNESCO definition of absolute poverty. Based on this definition, 10% or 6,998 households are considered poor. A backpropagation neural network is trained to distinguish households as either poor or not. We achieve an accuracy of about 61% on both the train and test sets. Further rule-extraction on the trained network is done in order to understand, in terms of the features used for training, which features contribute to the positive identification of households that are poor by UNESCO definition. To complement the extracted rules, the poverty dataset is fed to a Self-Organizing Map (SOM), which is then used to allow for an intuitive visualization of various facets of poverty. From the trained SOM, three distinct 'poverty' clusters were identified. |
Updated 07/09/2013