Journal Published Online: 31 January 2019
Volume 47, Issue 6

An Enhanced Hybrid Feature Selection Approach for High Dimensional Data Processing

CODEN: JTEVAB

Abstract

A huge volume of data is being generated day by day, each and every second, with mounting trends and technologies. Big data has evolved as an ultimate approach in data origination, attainment, and processing and also analysis, coping with the heterogeneity of the data in order to obtain useful insights from it. It is obvious that there is no point in having the data without quality. Hence, in order to use or leverage the data in a more apposite manner, data with quality are important. Numerous technologies are being developed with the evolution of big data. The input to those technologies and approaches is to be processed in such a way that they guarantee data quality, to yield better processing and results. An effective preprocessing approach is proposed in this article for the processing of big data. No single model can work well in the case of data processing, considering the challenges imposed with big data. Hence, a hybrid preprocessing approach is imposed here to deal with the data and processing, ensuring a better outcome. The results show that the approach enhances the quality of the data and is more effective. This approach, along with the big data platform using the Spark framework, increases the overall performance of the processing.

Author Information

Lincy, Blessy Trencia
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
Nagarajan, Suresh Kumar
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
Pages: 16
Price: $25.00
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Details
Stock #: JTE20180507
ISSN: 0090-3973
DOI: 10.1520/JTE20180507