The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2021.0121138
PDF

Machine Learning Driven Feature Sensitive Progressive Sampling Model for BigData Analytics

Author 1: Nandita Bangera
Author 2: Kayarvizhy N

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 11, 2021.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: BigData requires processing a huge data volume, which is an undeniable challenge for academia-industries. The classical sampling techniques are limited when addressing data-imbalance, large data-heterogeneity, multi-dimensionality etc. To alleviate it, in this paper a novel machine learning driven feature sensitive progressive sampling (ML-FSPS) that in conjunction with an improved feature selection and classification environment achieves more than 95.7% of accuracy, even with 10-14% of the original data size. The proposed ML-FSPS model was applied for IoT-device classification problem that possesses exceedingly high data-imbalance, multi-dimensionality and heterogeneity issues. Functionally, the FSPS-driven analytics model at first performed active period segmentation followed by multi-dimensional (descriptive) statistical feature extraction and Wilcoxon Rank Sum Test based feature selection. Subsequently, it executed K-Means clustering over a gigantically huge feature instances (16,00,000,000 network traces) Here, K-means algorithm clustered each feature samples into five distinct clusters. With initial sample size of 10%, FSPS model selected same amount of data elements (0.5-5% iteratively) from each cluster for each feature to perform multi-class classification using homogenous ensemble learning (HEL) model. Here HEL encompassed AdaBoost, Random Forest and Extended Tree ensemble algorithms as base classifiers. The simulation results affirmed that the proposed model achieves accuracy of almost 99% even with 10-16% of sample size.

Keywords: Feature sensitive progressive sampling; BigData analytics; machine learning; ensemble learning; rank sum test; IoT-device classification

Nandita Bangera and Kayarvizhy N, “Machine Learning Driven Feature Sensitive Progressive Sampling Model for BigData Analytics” International Journal of Advanced Computer Science and Applications(IJACSA), 12(11), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121138

@article{Bangera2021,
title = {Machine Learning Driven Feature Sensitive Progressive Sampling Model for BigData Analytics},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121138},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121138},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {11},
author = {Nandita Bangera and Kayarvizhy N}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org