CFP last date
01 August 2024
Reseach Article

Various Approaches for Multiclass Imbalance Learning Issues with MLP

by Ranjana Singh, Roshani Raut(Ade)
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 4
Year of Publication: 2016
Authors: Ranjana Singh, Roshani Raut(Ade)

Ranjana Singh, Roshani Raut(Ade) . Various Approaches for Multiclass Imbalance Learning Issues with MLP. Communications on Applied Electronics. 5, 4 ( Jun 2016), 13-16. DOI=10.5120/cae2016652277

@article{ 10.5120/cae2016652277,
author = { Ranjana Singh, Roshani Raut(Ade) },
title = { Various Approaches for Multiclass Imbalance Learning Issues with MLP },
journal = { Communications on Applied Electronics },
issue_date = { Jun 2016 },
volume = { 5 },
number = { 4 },
month = { Jun },
year = { 2016 },
issn = { 2394-4714 },
pages = { 13-16 },
numpages = {9},
url = { },
doi = { 10.5120/cae2016652277 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:54:45.453065+05:30
%A Ranjana Singh
%A Roshani Raut(Ade)
%T Various Approaches for Multiclass Imbalance Learning Issues with MLP
%J Communications on Applied Electronics
%@ 2394-4714
%V 5
%N 4
%P 13-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Imbalance data is a major issue, which can be either binary or multiclass. Oversampling, Undersampling, SMOTE, SMOTEboost, Adaboost, OSS (One Sided Selection) and many other algorithms are there to deal with binary or multiclass imbalance issues. Software Defect Predictors (SDPs) and Software Cost Estimations (SCEs) are tools that used to classify the software elements into certain factors which helps in studying the imbalance problem. First take into consideration the SDPs to predict the defect prone part of software so that project can be completed with expected quality. In the same way for SCEs, certain factors will need to be considered for overall cost estimation in a way that financials can be managed and software elements can be done neatly. Class imbalance learning challenges, supervised learning difficulties where some classes have significantly more samples than others, i.e. dataset having a set of majority and minority samples. To make imbalance data balanced, most of the present study focused only on binary-class cases. In this paper, Adaboost.NC method is introduced and its result will be analyzed with proposed Dynamic Sampling method-Multilayer Perceptrons ((DyS)-MLP) for multiclass imbalance problem.

  1. Shuo Wang, and Xin Yao, “Using Class Imbalance Learning for Software Defect Prediction”, member IEEE, IEEE Transaction on Reliability, vol. 62, no. 2, June 2013.
  2. Ade, Roshani, and P. R. Deshmukh. "An incremental ensemble of classifiers as a technique for prediction of student's career choice." Networks & Soft Computing (ICNSC), 2014 First International Conference on. IEEE, 2014.
  3. T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell, “A systematic review of fault prediction performance in software engineering,” IEEE Trans. Software Eng., vol. 38, no. 6, pp. 1276–1304, Nov.-Dec.2012.
  4. S. Wang and X. Yao, Negative Correlation Learning for Class Imbalance Problems School of Computer Science, University of Birmingham,2012.
  5. Ade, Roshani, and P. R. Deshmukh. "Classification of students by using an incremental ensemble of classifiers." Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 2014 3rd International Conference on. IEEE, 2014.
  6. C. Catal, “Software fault prediction: A literature review and current trends,” Expert Syst. Appl., vol. 38, no. 4, pp. 4626–4636, 2010.
  7. Ade, Roshani, and P. R. Deshmukh. "Efficient Knowledge Transformation System Using Pair of Classifiers for Prediction of Students Career Choice."Procedia Computer Science 46 (2015): 176-183.
  8. J. Zheng, “Cost-sensitive boosting neural networks for software defect prediction,” Expert Syst. Appl., vol. 37, no. 6, pp. 4537–4543, 2010.
  9. Ade, Roshani, and P. R. Deshmukh. "Instance-based vs Batch-based Incremental Learning Approach for Students Classification." International Journal of Computer Applications 106.3 (2014).
  10. H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Trans. Knowledge Data Eng., vol. 21, no. 9, pp. 1263–1284, Sep. 2009.
  11. Ade, Roshani, and Prashant Deshmukh. "Efficient knowledge transformation for incremental learning and detection of new concept class in student’s classification system." Information Systems Design and Intelligent Applications. Springer India, 2015. 757-766.
  12. T. Mu, J. Jiang, Y. Wang, and J. Y. Goulermas, “Adaptive data embedding framework for multiclass classification,” IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 8, pp. 1291–1303, Aug. 2012.
  13. Kulkarni, Pallavi Digambarrao and Roshani Ade. "Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model: A Novel Approach Using Machine Learning for Assessment of Credit Card Frauds." Handbook of Research on Natural Computing for Optimization Problems. IGI Global, 2016. 561-582. Web. 9 Jun. 2016. doi:10.4018/978-1-5225-0058-2.ch023
Index Terms

Computer Science
Information Sciences


Multiclass Imbalance Learning; Multilayer Perceptrons (MLP); Dynamic Sampling (DyS); Software Defect Prediction.