Evaluating the Performance of Two Hybrid Feature Selection Model of Machine Learning for Credit Card Fraud Detection on Classification and Prediction Methods
The hybrid and non-hybrid feature selection model aimed at predicting and classifying whether a transaction is fraudulent or non-fraudulent using machine learning approaches. The objective of this study was to use supervised learning framework to differentiate fraudulent and genuine transactions. The proposed hybrid model utilizes feature selection methods namely; the Principal Component Analysis (PCA) and PCA-Backward elimination with multiple linear regression and Reduced Error Pruning Tree classifier (RepT) using Python and WEKA. Through five experiments carried out in this study, the proposed approach has proven to be effective for eliminating redundant features in the dataset that does not have significant impact using PCA and Backward elimination method to optimize the predictive behaviour of the credit card transactions. Our first findings from the experimental results revealed that the RepT with PCA-Backward-Elimination prediction accuracy 87.37% is higher than that of multiple linear regressions with PCA 73.35% and PCA-Backward-Elimination 73.34%. Our second findings also revealed that the RepT with PCA-Backward-Elimination classification accuracy of 99.9368% is higher than that of multiple linear regressions with PCA 99.9122% and PCA-Backward-Elimination 99.9105%. The performance metrics measures on the classification model of the logistic regression with PCA-Backward-Elimination indicates that the hybrid model negates it with the expectation of maximization to minimization (99.9122% to 99.9105%) but returns the same results on the RepT decision tree classification in both cases of 99.9368%. The proposed hybrid feature selection method with machine learning algorithms outperforms the non-hybrid feature selection method with machine learning algorithms for classification and prediction accuracy.
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