Detection of fraud use of credit card by extended VFDT

Global society has experienced a flood of various types of data as well as a growing desire to discover and use this information effectively. Moreover, this data is changing in increasingly huge and complex ways. In particular, for data that is generated intermittently and at different intervals, attention has been focused on data streams that use sensor-network and stream mining technologies to discover useful information. In this paper, we focus on classification learning, which is an analytical method of stream mining. We are concerned with a decision tree learning called Very Fast Decision Tree learner (VFDT), which regards real data as a data stream. We analyze credit card transaction data as data stream and detect fraud use. In recent years, people with credit card are increasing. However, it also increases the damage of fraud use accordingly. Therefore, the detection of fraud use by data stream mining is demanded. However, for some data, such as credit card transaction data, contains extremely different rate of classes. Therefore, we propose and implement new statistical criteria to be used in a node-construction algorithm that implements VFDT. We also evaluate whether this method can be supported in imbalanced distribution data streams.