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Article Details

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.

Machine Learning Driven Feature Sensitive Progressive Sampling Model for BigData Analytics

Author 1: Nandita Bangera
Author 2: Kayarvizhy N

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0121138

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

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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}
}


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