Analysis of SMOTE: Modified for Diverse Imbalanced Datasets Under the IoT Environment

Ankita Bansal, Makul Saini, Rakshit Singh and Jai Kumar Yadav
Additional contact information
Ankita Bansal: Netaji Subhas University of Technology, India
Makul Saini: Netaji Subhas Institute of Technology, India
Rakshit Singh: Netaji Subhas Institute of Technology, India
Jai Kumar Yadav: Netaji Subhas Institute of Technology, India

International Journal of Information Retrieval Research (IJIRR), 2021, vol. 11, issue 2, 15-37

Abstract: The tremendous amount of data generated through IoT can be imbalanced causing class imbalance problem (CIP). CIP is one of the major issues in machine learning where most of the samples belong to one of the classes, thus producing biased classifiers. The authors in this paper are working on four imbalanced datasets belonging to diverse domains. The objective of this study is to deal with CIP using oversampling techniques. One of the commonly used oversampling approaches is synthetic minority oversampling technique (SMOTE). In this paper, the authors have suggested modifications in SMOTE and proposed their own algorithm, SMOTE-modified (SMOTE-M). To provide a fair evaluation, it is compared with three oversampling approaches, SMOTE, adaptive synthetic oversampling (ADASYN), and SMOTE-Adaboost. To evaluate the performances of sampling approaches, models are constructed using four classifiers (K-nearest neighbour, decision tree, naive Bayes, logistic regression) on balanced and imbalanced datasets. The study shows that the results of SMOTE-M are comparable to that of ADASYN and SMOTE-Adaboost.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJIRR.2021040102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jirr00:v:11:y:2021:i:2:p:15-37

Access Statistics for this article

International Journal of Information Retrieval Research (IJIRR) is currently edited by Zhongyu Lu

More articles in International Journal of Information Retrieval Research (IJIRR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().