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Attributes Reduction in Big Data

Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(14), 4901;
Received: 4 June 2020 / Revised: 8 July 2020 / Accepted: 10 July 2020 / Published: 17 July 2020
Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics. View Full-Text
Keywords: attributes sampling; content-based filtering; Support Vector Machines; machine learning attributes sampling; content-based filtering; Support Vector Machines; machine learning
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Albattah, W.; Khan, R.U.; Khan, K. Attributes Reduction in Big Data. Appl. Sci. 2020, 10, 4901.

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