Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were use...Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.展开更多
为了更好的进行垃圾邮件过滤 ,介绍了NaiveBayes (N B )算法 ,并结合N B 算法和垃圾邮件过滤对N B 算法作了一些改进。之后 ,详细介绍了N B 算法在垃圾邮件智能过滤中的应用和实现 ,同时给出了应用的评估算法和评估结果。实验结果显示 ,...为了更好的进行垃圾邮件过滤 ,介绍了NaiveBayes (N B )算法 ,并结合N B 算法和垃圾邮件过滤对N B 算法作了一些改进。之后 ,详细介绍了N B 算法在垃圾邮件智能过滤中的应用和实现 ,同时给出了应用的评估算法和评估结果。实验结果显示 ,把N B展开更多
文摘Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.
