Comparison of Various Machine Learning Techniques Based on Variable Selection under Imbalanced Data

Mo Ahsan Ahmad , Umme Kulsum , Faizan Ansari , Mo Nafees , Ravindranath Sawane

Imbalanced data, Variable Selection, Oversampling, Undersampling, Supervised Learning, Credit Card Fraud Detection

Classification in an imbalanced dataset is one of the challenges in statistical learning because many algorithms are designed to optimize overall accuracy without considering the relative class distribution. These algorithms are biased towards the majority class and tend to ignore the minority class, which is the class of interest for experimenters. This paper reviewed the data-level approach, algorithmic-level approach, and performance evaluation metrics for the classification of imbalanced data. Oversampling, undersampling, and hybrid sampling techniques are discussed along with Decision Trees Classifier, K-NN, Naive Bayes, Logistic Regression, Support Vector Machines, and Random Forest Classifier. The effectiveness of data-level approaches combined with algorithmic-level approaches to improve classification performance and compare the various machine learning classification algorithms of a “credit card fraud detection” dataset. Random Forest Classifier achieved the highest F-score and lowest log loss with and without resampling techniques.

Published Paper   E-Certificate

"Comparison of Various Machine Learning Techniques Based on Variable Selection under Imbalanced Data", IJEDR - INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH (www.IJEDR.org), ISSN:2321-9939, Vol.10, Issue 4, page no.60-70, December 2022, Available :https://rjwave.org/IJEDR/papers/IJEDR2204009.pdf

Volume 10 Issue 4, November-2022

Pages : 60-70

Paper Reg. ID: IJEDR_220223

Published Paper Id: IJEDR2204009

Research Area: Engineering

Country: Bhopal, Madhya Pradesh, India

Published Paper PDF: https://rjwave.org/IJEDR/papers/IJEDR2204009

Published Paper URL: https://rjwave.org/IJEDR/viewpaperforall?paper=IJEDR2204009

DOI: http://doi.one/10.1729/Journal.32282

ISSN: 2321-9939 | IMPACT FACTOR: 9.37 Calculated By Google Scholar | ESTD YEAR: 2013

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.37 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJEDR (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition