Large Dataset Classification Using Parallel Processing Concept

Aljanabi, Mohammad and Ebraheem, Hind Ra'ad and Hussain, Zahraa Faiz and Mohd Farhan, Md Fudzee and Shahreen, Kasim and Mohd Arfian, Ismail and Meidelfie, Dwiny and Eriandae, Aldo (2020) Large Dataset Classification Using Parallel Processing Concept. JOIV: International Journal on Informatics Visualization, 4 (4). pp. 191-194. ISSN 2549-9904. (Published)

Large Dataset Classification.pdf
Available under License Creative Commons Attribution Share Alike.

Download (992kB) | Preview


Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.

Item Type: Article
Uncontrolled Keywords: Large dataset; Parallel SVMs; PCA; Apache Spark.
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computing
Depositing User: Noorul Farina Arifin
Date Deposited: 12 Jan 2021 07:36
Last Modified: 12 Jan 2021 07:36
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item