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A Comparison of Supervised Learning Algorithms for the Income Classification

by Mohammed Temraz
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 38
Year of Publication: 2019
Authors: Mohammed Temraz
10.5120/ijca2019918391

Mohammed Temraz . A Comparison of Supervised Learning Algorithms for the Income Classification. International Journal of Computer Applications. 182, 38 ( Jan 2019), 19-25. DOI=10.5120/ijca2019918391

@article{ 10.5120/ijca2019918391,
author = { Mohammed Temraz },
title = { A Comparison of Supervised Learning Algorithms for the Income Classification },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 182 },
number = { 38 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 19-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number38/30314-2019918391/ },
doi = { 10.5120/ijca2019918391 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:13:40.459322+05:30
%A Mohammed Temraz
%T A Comparison of Supervised Learning Algorithms for the Income Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 38
%P 19-25
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The fundamental population data are needed for every country for purposes of planning, development, and improvement. Census data can provide the basic population data of any country. Moreover, they are rich with lots of hidden information that can be used for machine learning and data mining tasks in order to provide services for country's social and economic development. This paper is focused on the applications of data mining and machine learning in census data to classify the annual income. It aims to show a systematic comparison to examine and evaluate three supervised learning classifiers. The classifiers that have been targeted are decision trees, random forests, and artificial neural networks. The main aims are to explore not only the classifiers properties and the impact of the attributes on the evaluation, but also, evaluate their classification performance under certain conditions to understand how the performance of the models changes over different experiments which potentially provide a guidance to help researchers to determine the most suitable classifier in census data.

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Index Terms

Computer Science
Information Sciences

Keywords

Census Data Data Mining Classification Supervised Learning Decision Trees Random Forests Artificial Neural Networks Performance Metrics.

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