Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification

Malaysia is a developing country which relies on the monetary approach to measure poverty. The approach is simple to measure but it is insensitive towards changes of the poor in multiple dimensions such as education, health and living standards especially in urban areas. Several current issues in cl...

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Main Author:	Zakaria, Noor Hidayah
Format:	Thesis
Language:	English
Published:	2018
Subjects:	HT101-395 Sociology Urban HT101-395 Sociology, Urban
Online Access:	http://eprints.utm.my/id/eprint/98246/1/NoorHidayahZakariaPSC2018.pdf
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id	my-utm-ep.98246
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spelling	my-utm-ep.982462022-11-23T08:17:37Z Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification 2018 Zakaria, Noor Hidayah HT101-395 Sociology, Urban QA75 Electronic computers. Computer science Malaysia is a developing country which relies on the monetary approach to measure poverty. The approach is simple to measure but it is insensitive towards changes of the poor in multiple dimensions such as education, health and living standards especially in urban areas. Several current issues in classifying the urban poor include rigid dichotomy of the poor and non-poor, unable to capture changes that happens in various sub-groups of urban poor population and misclassified poverty indicators. This study developed a multidimensional poverty measurement framework which integrated i) Alkire-Foster approaches in quantification of multidimensional urban poor, ii) Adaptive Neural Fuzzy Inference Systems (ANFIS) to predict classification of urban poor and resolve the misclassification of urban poor and iii) ensemble ANFIS. 300 questionnaires were distributed to targeted households in Bandar Tasik Selatan, Kuala Lumpur. This study started with a comparison of datadriven Fuzzy Rule-Based System (FRBS) with the domain expert comprising FRBS classification. Next, the Alkire-Foster method was introduced which included parameter selection, dual cut off identification and aggregation of the poor. Then, the ANFIS prediction was carried out using various ANFIS combination models such as Genfis 1, Genfis 2 and Genfis 3 to predict the classification of urban poor. This study proceeded to improve the classification by proposing the ensemble ANFIS that included ensemble weighting and ensemble integration method. The performance of this proposed framework was evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), and R-Squared. For validation purposes, this study was reviewed by officers at the Zakat Collection Centre, Kuala Lumpur as the domain experts. The findings showed that the Genfis 3 using Fuzzy C-Means clustering algorithm in ANFIS outperformed all the ANFIS models, by obtaining the least MSE and RMSE values and highest R-Squared. These results included the Health dimension which was excluded in the current poverty measurement. Overall, this study has managed to address the urban poor classification by providing multiple dimensions of the poor and produce robust prediction results. 2018 Thesis http://eprints.utm.my/id/eprint/98246/ http://eprints.utm.my/id/eprint/98246/1/NoorHidayahZakariaPSC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:141941 phd doctoral Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HT101-395 Sociology Urban HT101-395 Sociology, Urban
spellingShingle	HT101-395 Sociology Urban HT101-395 Sociology, Urban Zakaria, Noor Hidayah Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification
description	Malaysia is a developing country which relies on the monetary approach to measure poverty. The approach is simple to measure but it is insensitive towards changes of the poor in multiple dimensions such as education, health and living standards especially in urban areas. Several current issues in classifying the urban poor include rigid dichotomy of the poor and non-poor, unable to capture changes that happens in various sub-groups of urban poor population and misclassified poverty indicators. This study developed a multidimensional poverty measurement framework which integrated i) Alkire-Foster approaches in quantification of multidimensional urban poor, ii) Adaptive Neural Fuzzy Inference Systems (ANFIS) to predict classification of urban poor and resolve the misclassification of urban poor and iii) ensemble ANFIS. 300 questionnaires were distributed to targeted households in Bandar Tasik Selatan, Kuala Lumpur. This study started with a comparison of datadriven Fuzzy Rule-Based System (FRBS) with the domain expert comprising FRBS classification. Next, the Alkire-Foster method was introduced which included parameter selection, dual cut off identification and aggregation of the poor. Then, the ANFIS prediction was carried out using various ANFIS combination models such as Genfis 1, Genfis 2 and Genfis 3 to predict the classification of urban poor. This study proceeded to improve the classification by proposing the ensemble ANFIS that included ensemble weighting and ensemble integration method. The performance of this proposed framework was evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), and R-Squared. For validation purposes, this study was reviewed by officers at the Zakat Collection Centre, Kuala Lumpur as the domain experts. The findings showed that the Genfis 3 using Fuzzy C-Means clustering algorithm in ANFIS outperformed all the ANFIS models, by obtaining the least MSE and RMSE values and highest R-Squared. These results included the Health dimension which was excluded in the current poverty measurement. Overall, this study has managed to address the urban poor classification by providing multiple dimensions of the poor and produce robust prediction results.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zakaria, Noor Hidayah
author_facet	Zakaria, Noor Hidayah
author_sort	Zakaria, Noor Hidayah
title	Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification
title_short	Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification
title_full	Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification
title_fullStr	Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification
title_full_unstemmed	Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification
title_sort	alkire-foster oriented ensemble fuzzy inference system for urban poverty classification
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing
granting_department	Faculty of Engineering - School of Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/98246/1/NoorHidayahZakariaPSC2018.pdf
_version_	1776100565257814016

Alkire-Foster oriented ensemble fuzzy inference system for urban poverty classification

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