With the development of Internet and technology, credit cards are more widely used transaction data larger. The set card fraud is a typical imbalanced problem. model should ensure that detected customer service quality guaranteed. Improving both precision recall rate focus current research. However, when constrained by level machine learning, it good choice to use different models evaluate obta...

This paper proposes a method, called autoencoder with probabilistic random forest (AE-PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the to extract features of low-dimensionality from transaction data high-dimensionality. It then relies on forest, an ensemble learning mechanism using bootstrap aggregating (bagging) concept, classification classify as fraudulen...

The rising number of credit card frauds presents a significant challenge for the banking industry. Many businesses and financial institutions suffer huge losses because users are reluctant to use their cards. A primary goal fraud detection is identify prior transaction patterns detect future fraud. In this paper, hybrid ensemble model proposed combine bagging boosting techniques distinguish bet...

Very large databases with skewed class distributions and non-unlform cost per error are not uncommon in real-world data mining tasks. We devised a multi-classifier meta-learning approach to address these three issues. Our empirical results from a credit card fraud detection task indicate that the approach can significantly reduce loss due to illegitimate transactions.