Neural network rule extraction for gaining insight into the characteristics of poverty

• Main Authors: Azcarraga, Arnulfo P., Setiono, Rudy
• Format: text
• Published: Animo Repository 2018
• Subjects: Back propagation (Artificial intelligence); Poverty—Data processing; Neural networks (Computer science); Software Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2098
• Institution: De La Salle University

Nearly one in five families in the country was poor in 2012, according to the Philippine Statistics Authority. While this proportion is lower than the corresponding figures from 2006 and 2009, the absolute number of poor families has actually grown from 3.8 million in 2006 to 4.2 million in 2012 due to the increase in population. Using data samples that have been collected from 69,130 households through a comprehensive community-based monitoring survey conducted in one of the cities that comprise Metro Manila, we attempt to identify the characteristics that differentiate between poor and non-poor households. Using back-propagation neural networks, we are able to correctly predict 73% of the poor households and 60% of the non-poor households. Moreover, the rules extracted from one of these networks provide concise description of how households are classified as poor based on their demographic characteristics and information pertaining to their surrounding living conditions. © 2017, The Natural Computing Applications Forum.