Clustering Alkire Foster-Oriented Quantification in Measuring Multidimensional Poverty Indicators by Using Intelligent Adaptive Neural Fuzzy Inference Systems

Abstract

Malaysia is a developing country which relies on the monetary approach when it comes to poverty measurement. The current monetary approach is simpler to measure; however, it is insensitive towards changes of the poor in multiple dimensions especially in urban area. Based on household survey data on urban province in Malaysia, this study proposes on a multidimensional poverty measurement framework, which predicts on the prominent deprived indicators based on multidimensional urban poor measurement, replacing the conventional money-metric measure. This study highlights on integration between Alkire-Foster approaches in quantification of multidimensional urban poor with Adaptive Neural Fuzzy Inference Systems (ANFIS). By addressing the deprived indicator in urban area, the combination of Alkire Foster and ANFIS approach could efficiently resolve on the issue of misfit urban poor in the country. In this study, Alkire Foster approach is proven to have promising results in improving the determination of the urban poor in Malaysia. In future, this study aims in addressing the particular combination of indicator that causes the urban poverty in Malaysia.