Ensemble Learning for Multidimensional Poverty Classification

Bakar, Azuraliza Abu; Hamdan, Rusnita; Sani, Nor Samsiah

doi:10.17576/jsm-2020-4902-24

Cited by 26 publications

(30 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indicators and dimensions are two most important components of MPI in defining poverty. Indicators should capture the deprivation experienced, while dimensions are the grouping of indicators [ 46 , 47 ]. There are many methods for selecting MPI indicators and dimensions.…”

Section: Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

et al. 2021

Self Cite

View full text Add to dashboard Cite

The Multidimensional Poverty Index (MPI) is an income-based poverty index which measures multiple deprivations alongside other relevant factors to determine and classify poverty. The implementation of a reliable MPI is one of the significant efforts by the Malaysian government to improve measures in alleviating poverty, in line with the recent policy for Bottom 40 Percent (B40) group. However, using this measurement, only 0.86% of Malaysians are regarded as multidimensionally poor, and this measurement was claimed to be irrelevant for Malaysia as a country that has rapid economic development. Therefore, this study proposes a B40 clustering-based K-Means with cosine similarity architecture to identify the right indicators and dimensions that will provide data driven MPI measurement. In order to evaluate the approach, this study conducted extensive experiments on the Malaysian Census dataset. A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. The clustering model produced eight clusters of B40 group. The study included a comprehensive clustering analysis to meaningfully understand each of the clusters. The analysis discovered seven indicators of multidimensional poverty from three dimensions encompassing education, living standard and employment. Out of the seven indicators, this study proposed six indicators to be added to the current MPI to establish a more meaningful scenario of the current poverty trend in Malaysia. The outcomes from this study may help the government in properly identifying the B40 group who suffers from financial burden, which could have been currently misclassified.

show abstract

Section: Results and Analysismentioning

confidence: 99%

“…Clustering technique is a method of collecting data objects and grouping them based on the similarity of objects to gain an in-depth understanding of data distribution. In general, there are five key approaches to clustering, namely partitioning, hierarchy, density-based, grid-based and model-based [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…These RF links are not only costly but also consume large power and it is impractical. Compared with the microwave band, the aperture of antenna elements in the millimeter-wave band is usually smaller, and a large number of antenna elements can be integrated at the transmitting end of the millimeter-wave system, thereby using multiple-input multiple-output (MIMO) technology to improve antenna gain through beamforming; at the same time, can solve the problem of high path loss and attenuation in the millimeter-wave band, it also precode multiple data streams for multiple users, improving the spectral efficiency of the system [5][6][7][8]. Generally, microwave band communication systems are precoded at the baseband by digital signal processing (DSP) units.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

Shahjehan¹,

Ullah²,

Shah³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Millimeter-Wave (mmWave) Massive MIMO is one of the most effective technology for the fifth-generation (5G) wireless networks. It improves both the spectral and energy efficiency by utilizing the 30-300 GHz millimeter-wave bandwidth and a large number of antennas at the base station. However, increasing the number of antennas requires a large number of radio frequency (RF) chains which results in high power consumption. In order to reduce the RF chain's energy, cost and provide desirable quality-ofservice (QoS) to the subscribers, this paper proposes an energy-efficient hybrid precoding algorithm for mmWave massive MIMO networks based on the idea of RF chains selection. The sparse digital precoding problem is generated by utilizing the analog precoding codebook. Then, it is jointly solved through iterative fractional programming and successive convex optimization (SCA) techniques. Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

show abstract

“…Machine learning is a data analysis tool that automates the creation of analysis models. It is an artificial intelligence branch focused on the concept that systems can learn from data, detect patterns, and make choices with minimal human intervention [1][2][3][4]. Machine learning is the most popular and dominant approach to decision making because of the ability to automate complex tasks [3], [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Predicting COVID-19 Based on Environmental Factors With Machine Learning

Abdulkareem¹,

Sani²,

Sahran³

et al. 2021

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The coronavirus disease 2019 has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases and weather variables such as temperature, humidity, wind and precipitation are developed. This study aims to identify the best classification model to classify COVID-19 by using significant weather features chosen by Principle Component Analysis (PCA) feature selection method. The DS4C COVID-19 dataset is used to train and validate each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by PCA. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized CNN classifier with seven weather variables selected by PCA achieved the highest performance among all the techniques. The experimental results obtained show that the weather variables are more relevant in predicting the confirmed cases as compared to the other variables. Thus, from this result, it is evident that temperature, humidity, wind and precipitation are important features for predicting COVID-19 confirmed cases.

show abstract

Ensemble Learning for Multidimensional Poverty Classification

Cited by 26 publications

References 0 publications

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group

A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

Predicting COVID-19 Based on Environmental Factors With Machine Learning

Contact Info

Product

Resources

About