Open Access Open Access  Restricted Access Subscription or Fee Access

Comparative Study on Feature Selection Methods to Reduce High Dimensionality in Big Data

Dr. S. Banumathi, A. Balasathya


Big information may be a combination of structured, semi structured and unstructured information collected by organizations that may be strip-mined for info and employed in machine learning comes, prognosticative modeling and different advanced analytics application. Spatiality in statistics refers to what number attributes a dataset has, care information is ill-famed for having Brobdingnagian amounts of variables in a perfect world; this information may be depicted in a very unfold sheet, with one column representing every dimension. In observe, this can be tough to try to, in past as a result of several variables square measure inter-related (like weight and blood pressure).This paper gift study on feature choice technique to cut back high spatiality issue in huge information.


Big Data, High Dimensionality, Feature Selection, Filter, Wrapped, Embedded, Hybrid.

Full Text:



Chaiken, Ronnie, et al. "HD: easy and efficient parallel processing of massive data sets." Proceedings of the VLDB Endowment 1.2 ,2019: 1265-1276.

Paul Zikopoulos, Chris Eaton, et al. hybrid Understanding big data Analytics for enterprise class hadoop and streaming data McGraw-Hill Osborne Media, 2019.

Curbera, Francisco, et al. "Unraveling the Web services web an introduction to embedded data , WSDL, and UDDI." sIEEE Interne computing 6.2, 2017 :86-93.

Dean, Jeffrey, and Sanjay Ghemawat. "Wrapped : simplified data processing on large clusters."Communications of the ACM 51.1 ,2018: 107-113.

Fielding, Roy Thomas. Architectural styles and the design of network-based software architectures. Diss. University of California, Irvine, 2019.

Microsoft Azure HDInsight.

Erich Gamma, Richard Helm, Ralph E. Johnson, and John Vlissides. Design patterns: elements of reusable objectoriented software, volume 206. 2017.


  • There are currently no refbacks.