Open Access   Article Go Back

A Novel Algorithm for Class Imbalance Learning on Big Data using Uniform Sampling Strategy (USS) Technique

Mohammad Imran1

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-3 , Page no. 503-506, Mar-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i3.503506

Online published on Mar 30, 2018

Copyright © Mohammad Imran . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Mohammad Imran, “A Novel Algorithm for Class Imbalance Learning on Big Data using Uniform Sampling Strategy (USS) Technique,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.503-506, 2018.

MLA Style Citation: Mohammad Imran "A Novel Algorithm for Class Imbalance Learning on Big Data using Uniform Sampling Strategy (USS) Technique." International Journal of Computer Sciences and Engineering 6.3 (2018): 503-506.

APA Style Citation: Mohammad Imran, (2018). A Novel Algorithm for Class Imbalance Learning on Big Data using Uniform Sampling Strategy (USS) Technique. International Journal of Computer Sciences and Engineering, 6(3), 503-506.

BibTex Style Citation:
@article{Imran_2018,
author = {Mohammad Imran},
title = {A Novel Algorithm for Class Imbalance Learning on Big Data using Uniform Sampling Strategy (USS) Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2018},
volume = {6},
Issue = {3},
month = {3},
year = {2018},
issn = {2347-2693},
pages = {503-506},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5296},
doi = {https://doi.org/10.26438/ijcse/v6i3.503506}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i3.503506}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5296
TI - A Novel Algorithm for Class Imbalance Learning on Big Data using Uniform Sampling Strategy (USS) Technique
T2 - International Journal of Computer Sciences and Engineering
AU - Mohammad Imran
PY - 2018
DA - 2018/03/30
PB - IJCSE, Indore, INDIA
SP - 503-506
IS - 3
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
142 132 downloads 97 downloads
  
  
           

Abstract

Big data consists of large volumes of data which are used to discover the hidden knowledge. Class imbalance nature is a conventional issue which is present in all real world datasets. The class imbalance nature in the big data reduces the performance of the existing classification algorithms. The data source of diverse nature available from varied sources also degrades the performance of the existing algorithms. To address these issues of class imbalance problem the present work proposed various novel and effective class imbalance learning (CIL) algorithms. In this work, we proposed Uniform Strategic Sampling (USS) Technique novel algorithms approaches for class imbalance data sources.

Key-Words / Index Term

Class Imbalance Learning(CIL),Big Data,Sampling,Uniform Sampling Strategy Technique,Classification

References

[1]. Rukshan Batuwita and Vasile Palade, “CLASS IMBALANCE LEARNING METHODS FOR SUPPORT VECTOR MACHINES”, Imbalanced Learning: Foundations, Algorithms, and Applications, By Haibo He and Yunqian
Ma, Copyright c 2012 John Wiley & Sons, Inc.
[2]. Rushi Longadge, Snehlata S. Dongre, Latesh Malik,” Class Imbalance Problem in Data Mining: Review”, International Journal of Computer Science and Network (IJCSN) Volume 2, Issue 1, February 2013. www.ijcsn.org ISSN 2277-5420.
[3]. Kun Jiang, Jing Lu, Kuiliang Xia,” A Novel Algorithm for Imbalance Data Classification Based on Genetic Algorithm Improved SMOTE”, Arab J Sci. Eng, DOI 10.1007/s13369-016-2179-2.
[4]. Shaza M. Abd Elrahman and Ajith Abraham, “A Review of Class Imbalance Problem” Journal of Network and Innovative Computing ISSN 2160-2174, Volume 1, pp. 332-340, 2013. ©MIR Labs, www.mirlabs.net/jnic/index.html
[5]. Bartosz Krawczyk,” Learning from imbalanced data: open challenges and future directions”, Prog Artif Intell, DOI.10.1007/s13748-016-0094-0.