Credit Card Fraud Detection using BPSO based Features and Random Forest Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.10 · Issue 7

Published

February 2022

Access

Open Access

Licence

CC BY-NC-SA 4.0

Dr. Hemant N. Patel Dr. Amit N. Patel Mr. Sunil P. Patel

Credit card fraud is a social problem that faces many ethical challenges and poses a serious threat to businesses around the world. Machine learning algorithms are used to detect fraudulent transactions by authors. This study presents an implementation of an automated credit card fraud detection system where pre-processing the data, among others. Binary particle swarm optimization (BPSO) algorithms are used for the selection of features with a random forest (RF) classifier for training and testing from the Kaggle dataset. Sensitivity, precision, f-score, and accuracy are used as performance evaluation tools to evaluate the proposed technique.

RF Kaggle BPSO etc.

Dr. Hemant N. Patel, Dr. Amit N. Patel, Mr. Sunil P. Patel (2022). Credit Card Fraud Detection using BPSO based Features and Random Forest Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.10, Issue 7. ISSN: 2319-4863.

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