Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/3685
Title: Data balancing for credit card fraud detection using complementary neural networks and SMOTE algorithm
Authors: Shah, Vrushal
Keywords: Fraud detection;complementary neural network;SMOTE;oversampling;under sampling;class imbalance
Issue Date: 26-Aug-2020
Abstract: This Research presents an innovative approach towards detecting fraudulent credit card transactions. A commonly prevailing yet dominant problem faced in detection of fraudulent credit card transactions is the scarce occurrence of such fraudulent transactions with respect to legitimate (authorized) transactions. Therefore, any data that is recorded will always have a stark imbalance in the number of minority (fraudulent) and majority (legitimate) class samples. This imbalanced distribution of the training data among classes makes it hard for any learning algorithm to learn the features of the minority class. In this thesis work, we analyze the impact of applying class-balancing techniques on the training data namely oversampling (using SMOTE algorithm) for minority class and under sampling (using CMTNN) for majority class. The usage of most popular classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF) are processed on balanced data and which results to quantify the performance improvement provided by our approach. The experiments show that the hybrid approach which integrates Complementary Neural Network and Synthetic Minority Oversampling Technique gives a Quantitative performance in terms of Accuracy of 99% and 99.7% of AUC with Random Forest Classification Algorithm compared to simple undersampling and oversampling.
URI: https://zone.biblio.laurentian.ca/handle/10219/3685
Appears in Collections:Computational Sciences - Master's theses

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