A survey of multiple classifier systems as hybrid systems <s> Credit card fraud <s> In this paper, a four-stage support vector machine (SVM) based multiagent ensemble learning approach is proposed for credit risk evaluation. In the first stage, the initial dataset is divided into two independent subsets: training subset (in-sample data) and testing subset (out-of-sample data) for training and verification purposes. In the second stage, different SVM learning paradigms with much dissimilarity are constructed as intelligent agents for credit risk evaluation. In the third stage, multiple individual SVM agents are trained using training subsets and the corresponding evaluation results are also obtained. In the final stage, all individual results produced by multiple SVM agents in the previous stage are aggregated into an ensemble result. In particular, the impact of the diversity of individual intelligent agents on the generalization performance of the SVM-based multiagent ensemble learning system is examined and analyzed. For illustration, one corporate credit card application approval dataset is used to verify the effectiveness of the SVM-based multiagent ensemble learning system. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Credit card fraud <s> Credit card fraud is a serious and growing problem. While predictive models for credit card fraud detection are in active use in practice, reported studies on the use of data mining approaches for credit card fraud detection are relatively few, possibly due to the lack of available data for research. This paper evaluates two advanced data mining approaches, support vector machines and random forests, together with the well-known logistic regression, as part of an attempt to better detect (and thus control and prosecute) credit card fraud. The study is based on real-life data of transactions from an international credit card operation. <s> BIB002
Specific works on credit card fraud detection use real-life data of transactions from an international creditcard operation BIB002 . The exploration of the sensitivity to the ratio of fraud to non-fraud of the random undersampling approach to deal with unbalanced class sizes is required to validate the approaches. Comparing RF against SVM and logisti regression BIB002 , RF was the best performer in all experimental conditions as measured by almost all performance measurements. Other approaches to this problem include a bagged ensemble of SVM tested on a british card application approval dataset BIB001 .