A survey of multiple classifier systems as hybrid systems <s> Fraud detection <s> Fraud is increasing dramatically with the expansion of modem technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Fraud detection <s> Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. <s> BIB002
Fraud detection involves identifying fraud as soon as possible after it has been perpetrated. Fraud detection BIB001 is big area of research and applications of machine learning, which has provided techniques to counteract fraudsters in credit card fraud, money laundering, telecommunications fraud, and computer intrusion. MCS have been also applied successfully in this domain. A key task is modeling the normal behavior in order to be able to establish suspicion scores for outliers. Probabilistic networks are specific one-class classifiers that are well suited to this task, and bagging of probabilistic networks has been proposed as a general tool for fraud detection because the MCS approach improves the robustness of the normal behavior modeling BIB002 .